The Last Data Analysis to Rule Them All? (study1adults)
Adam Stone, PhD
01-26-2018
Putting It All Back Together
Throughout all the data analysis we’ve done, the datasets have become more fragmented - lexical recall, gist, and eye tracking datasets. I want to put them all together in one whole dataset again so we can perform some analyses more efficiently (particularly correlations). The only thing I need to remember is we’ll have a new column called eye_exclude and if it is set to TRUE it means we can’t include that row in any analysis relating to eye gaze (usually because that trial was less than 25% looking).
# Libraries
library(tidyverse)
library(lme4)
library(lmerTest)
library(scales)
library(viridis)
library(agricolae)
library(GGally)
library(ez)
# Load lex and eye data
cleanlexdata <- read_csv("cleandata.csv") %>%
select(-(forehead:total))
cleaneyedata <- read_csv("cleanpercentdata.csv") %>%
spread(aoi,percent) %>%
add_column(eye_exclude = FALSE)
# What rows were removed from the eye data back in 03eyegaze? Let's add back in
# With a new column - eye_exclude
removed <- anti_join(cleanlexdata, cleaneyedata) %>%
add_column(eye_exclude = TRUE)
eyelexdata <- bind_rows(cleaneyedata, removed)
# Load gist data
gist <- read_csv('gist_indiv.csv', col_types = cols(
participant = col_character(),
gist.fw1 = col_integer(),
gist.rv2 = col_integer(),
gist.fw3 = col_integer(),
gist.rv4 = col_integer()
)) %>%
gather(video, gist, gist.fw1:gist.rv4) %>%
mutate(video = str_sub(video,6,8))
# Presto, our full reunified dataset - 'fulldata'
# But I want to remove columns I don't want anymore and will recalculate later
fulldata <- left_join(eyelexdata, gist) %>%
select(-moutheye, -facechest, -face, -chest)Group Changes and Participant Tables
We have some changes to make to the groups. First, fix Josh as learning ASL when he was 6. Next, drop the DeafNative Group and reclassify all who learned ASL < 3.9 as DeafEarly and ASL => 4.0 as DeafLate.
# Change Josh's AoASL to 6
fulldata <- fulldata %>%
mutate(aoasl = as.double(aoasl)) %>%
mutate(aoasl = case_when(
participant == "Josh" ~ 6,
TRUE ~ aoasl
))
# Reclassify Groups
fulldata <- fulldata %>%
mutate(maingroup = case_when(
hearing == "Deaf" & aoasl < 4 ~ "DeafEarly",
hearing == "Deaf" & aoasl >= 4 ~ "DeafLate",
maingroup == "HearingLateASL" ~ "HearingLate",
maingroup == "HearingNoviceASL" ~ "HearingNovice"
))
# Create Participant Demographics Table
participant_info <- fulldata %>%
select(-(acc:gist)) %>%
select(-(video:direction)) %>%
distinct() %>%
group_by(maingroup) %>%
summarise(n = n(),
age_mean = mean(age),
age_sd = sd(age),
aoasl_mean = mean(aoasl),
aoasl_sd = sd(aoasl),
signyrs_mean = mean(signyrs),
signyrs_sd = sd(signyrs),
selfrate_mean = mean(selfrate),
selfrate_sd = sd(selfrate)) %>%
ungroup() %>%
mutate_if(is.double, funs(round(., 2))) %>%
mutate(age = paste(age_mean, "±", age_sd, sep = " "),
aoasl = paste(aoasl_mean, "±", aoasl_sd, sep = " "),
signyrs = paste(signyrs_mean, "±", signyrs_sd, sep = " "),
selfrate = paste(selfrate_mean, "±", selfrate_sd, sep = " ")) %>%
select(-(age_mean:selfrate_sd))
participant_infowrite_csv(fulldata, "finaldataset.csv")
data_lowaoi <- fulldata %>% select(participant, story, belly:upperchest) %>% select(-eyes, -mouth, -chin) %>% gather(aoi, percent, belly:upperchest)
data_lowaoi$percent[is.na(data_lowaoi$percent)] <- 0
mean(data_lowaoi$percent, na.rm=TRUE)[1] 0.007421356sd(data_lowaoi$percent, na.rm=TRUE)[1] 0.02793204Participant ANOVAs
Below are the ANOVA outputs for participant demographics, and LSDs for each.
Participants’ age
Df Sum Sq Mean Sq F value Pr(>F)
maingroup 3 1810 603.3 14.29 8.75e-07 ***
Residuals 48 2026 42.2
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1Participants’ AoASL
Df Sum Sq Mean Sq F value Pr(>F)
maingroup 3 2553.9 851.3 89.98 <2e-16 ***
Residuals 48 454.1 9.5
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1Participants’ Sign Yrs
Df Sum Sq Mean Sq F value Pr(>F)
maingroup 3 7032 2344.1 59.37 3.52e-16 ***
Residuals 48 1895 39.5
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1Participants’ Self-Rating
Df Sum Sq Mean Sq F value Pr(>F)
maingroup 3 30.71 10.237 72.37 <2e-16 ***
Residuals 48 6.79 0.141
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1Gist & Lexical Recall Data
Tables & Charts
Let’s generate a table for lexical recall and gist for forward vs. reversed stories.
lexgist_info <- fulldata %>%
group_by(maingroup, direction) %>%
summarise(lex_mean = mean(acc, na.rm = TRUE),
lex_sd = sd(acc, na.rm = TRUE),
gist_mean = mean(gist),
gist_sd = sd(gist)) %>%
ungroup() %>%
mutate_if(is.double, funs(round(., 2))) %>%
mutate(lex = paste(lex_mean, "±", lex_sd, sep = " "),
gist = paste(gist_mean, "±", gist_sd, sep = " ")) %>%
select(-(lex_mean:gist_sd)) %>%
gather(metric, value, lex:gist) %>%
unite("metric", c(metric, direction), sep = "_") %>%
spread(metric, value) %>%
print()And then bar charts too after that with error bars.
# Gist bar chart
gist_bar <- fulldata %>% select(participant, maingroup, direction, gist) %>%
group_by(maingroup, participant, direction) %>%
summarise(gist = mean(gist)) %>%
group_by(maingroup, direction) %>%
summarise(mean = mean(gist),
sd = sd(gist),
count = n(),
se = sd/sqrt(count)) %>%
ungroup() %>%
mutate(maingroup = case_when(
maingroup == "DeafEarly" ~ "Deaf Early",
maingroup == "DeafLate" ~ "Deaf Late",
maingroup == "HearingLate" ~ "Hearing Late",
maingroup == "HearingNovice" ~ "Hearing Novice"
))
ggplot(gist_bar, aes(x = maingroup, y = mean, fill = direction)) +
geom_bar(stat = "identity", position = position_dodge()) +
geom_errorbar(aes(ymin = mean-se, ymax = mean+se), position = position_dodge(0.9), width = 0.5) +
labs(title = "Gist", x = "", y = "mean gist accuracy") +
scale_y_continuous(labels = percent, limits = c(0,1)) + theme_bw()
# Lex bar chart
lex_bar <- fulldata %>% select(participant, maingroup, direction, acc) %>%
group_by(maingroup, participant, direction) %>%
summarise(acc = mean(acc, na.rm = TRUE)) %>%
group_by(maingroup, direction) %>%
summarise(mean = mean(acc, na.rm = TRUE),
sd = sd(acc, na.rm = TRUE),
count = n(),
se = sd/sqrt(count)) %>%
ungroup() %>%
mutate(maingroup = case_when(
maingroup == "DeafEarly" ~ "Deaf Early",
maingroup == "DeafLate" ~ "Deaf Late",
maingroup == "HearingLate" ~ "Hearing Late",
maingroup == "HearingNovice" ~ "Hearing Novice"
))
ggplot(lex_bar, aes(x = maingroup, y = mean, fill = direction)) +
geom_bar(stat = "identity", position = position_dodge()) +
geom_errorbar(aes(ymin = mean-se, ymax = mean+se), position = position_dodge(0.9), width = 0.5) +
labs(title = "Lexical Recall", x = "", y = "mean lexical recall accuracy") +
scale_y_continuous(labels = percent, limits = c(0,1)) +
geom_hline(yintercept = .5, linetype = "dotted") +
coord_cartesian(ylim = c(.5,1)) + theme_bw()
And let’s calculate the average reduction in score due to reversal first is lex recall, then gist.
reversal_lex <- fulldata %>%
group_by(id, direction) %>%
summarise(lex_mean = mean(acc, na.rm = TRUE)) %>%
spread(direction, lex_mean) %>%
group_by(id) %>%
mutate(reversal = forward - reversed) %>%
ungroup()
paste("lex mean", mean(reversal_lex$reversal))[1] "lex mean 0.141570192307692"paste("lex sd", sd(reversal_lex$reversal))[1] "lex sd 0.102655144785552"reversal_gist <- fulldata %>%
group_by(id, direction) %>%
summarise(gist_mean = mean(gist, na.rm = TRUE)) %>%
spread(direction, gist_mean) %>%
group_by(id) %>%
mutate(reversal = forward - reversed) %>%
ungroup()
paste("gist mean", mean(reversal_gist$reversal))[1] "gist mean 0.451923076923077"paste("gist sd", sd(reversal_gist$reversal))[1] "gist sd 0.39925930667527"ANOVA Plan
Next, we’re going to do ANOVAs. We’ll always do it in this order.
- ANOVA with factors MainGroup & Direction
- ANOVA with factor MainGroup, for Forward only
- ANOVA with factor MainGroup, for Reverse only
(I also ran ANCOVAs before but now have taken them out…they are below: (4) ANCOVA with factor Direction, and covariate AoASL and Age, (5) Regression with variables AoASL and Age, for Forward only, (6) Regression with variables AoASL and Age, for Reverse only.)
I did not include Age as a covariate in the first 3 ANOVAs because they did not add to or change the model in any significant way.
Gist ANOVAs
- ANOVA with factors MainGroup & Direction.
# First let's make the participant-level dataset with which we'll do our ANCOVAs.
participant_data <- fulldata %>%
group_by(maingroup, participant, direction) %>%
mutate(gist = mean(gist, na.rm = TRUE),
acc = mean(acc, na.rm = TRUE)) %>%
ungroup() %>%
select(id, participant, hearing, maingroup, direction, age, aoasl, acc, gist) %>%
distinct() %>%
mutate(id = factor(id),
participant = factor(participant),
hearing = factor(hearing),
maingroup = factor(maingroup),
direction = factor(direction))
# # Gist ANOVA 1
# gist_aov1 <- aov(gist ~ maingroup * direction, data = participant_data)
# summary(gist_aov1)
# gist_lsd1 <- LSD.test(gist_aov1, "maingroup", group = FALSE)
# gist_lsd1$comparison
# Gist EZ ANOVA
ezANOVA(
data = participant_data,
dv = gist,
wid = id,
within = direction,
between = maingroup,
type = 3
)["ANOVA"]Data is unbalanced (unequal N per group). Make sure you specified a well-considered value for the type argument to ezANOVA().$ANOVANA- ANOVA with factor MainGroup, for Forward only. In the code is a Kruskal-Wallis test. And Chi-Sq too.
# # Gist ANOVA 2
# gist_aov2 <- aov(gist ~ maingroup, data = filter(participant_data, direction == "forward"))
# summary(gist_aov2)
# gist_lsd2 <- LSD.test(gist_aov2, "maingroup", group = FALSE)
# gist_lsd2$comparison
ezANOVA(
data = filter(participant_data, direction == "forward"),
dv = gist,
wid = id,
between = maingroup,
type = 3
)["ANOVA"]Data is unbalanced (unequal N per group). Make sure you specified a well-considered value for the type argument to ezANOVA().$ANOVA# # KW Non-parametric test (like one-way ANOVA)
# kruskal.test(gist ~ maingroup, data = as.matrix(filter(participant_data, direction == "forward")))
#
# # Chi Sq
# gist_chisq_fw <- participant_data %>%
# ungroup() %>%
# filter(direction == "forward") %>%
# select(maingroup, gist) %>%
# group_by(maingroup, gist) %>%
# summarise(count = n()) %>%
# spread(gist, count) %>%
# rename(none = "0",
# one = "0.5",
# both = "1")
#
# gist_chisq_fw[is.na(gist_chisq_fw)] <- 0L
# gist_chisq_fw <- cbind(gist_chisq_fw[,"none"], gist_chisq_fw[,"one"], gist_chisq_fw[,"both"])
# chisq.test(gist_chisq_fw)- ANOVA with factor MainGroup, for Reverse only. In the code is a Kruskal-Wallis test. And Chi-Sq too.
# # Gist ANOVA 3
# gist_aov3 <- aov(gist ~ maingroup, data = filter(participant_data, direction == "reversed"))
# summary(gist_aov3)
# gist_lsd3 <- LSD.test(gist_aov3, "maingroup", group = FALSE)
# gist_lsd3$comparison
ezANOVA(
data = filter(participant_data, direction == "reversed"),
dv = gist,
wid = id,
between = maingroup,
type = 3
)["ANOVA"]Data is unbalanced (unequal N per group). Make sure you specified a well-considered value for the type argument to ezANOVA().$ANOVA# # KW Non-parametric test (like one-way ANOVA)
# kruskal.test(gist ~ maingroup, data = as.matrix(filter(participant_data, direction == "reversed")))
#
# # Chi Sq
# gist_chisq_rv <- participant_data %>%
# ungroup() %>%
# filter(direction == "reversed") %>%
# select(maingroup, gist) %>%
# group_by(maingroup, gist) %>%
# summarise(count = n()) %>%
# spread(gist, count) %>%
# rename(none = "0",
# one = "0.5",
# both = "1")
#
# gist_chisq_rv[is.na(gist_chisq_rv)] <- 0L
# gist_chisq_rv <- cbind(gist_chisq_rv[,"none"], gist_chisq_rv[,"one"], gist_chisq_rv[,"both"])
# chisq.test(gist_chisq_rv)Lexical Recall ANOVAs
- ANOVA with factors MainGroup & Direction.
# # Lexical Recall ANOVA 1
# acc_aov1 <- aov(acc ~ maingroup * direction, data = participant_data)
# summary(acc_aov1)
# acc_lsd1 <- LSD.test(acc_aov1, "maingroup", group = FALSE)
# acc_lsd1$comparison
ezANOVA(
data = participant_data,
dv = acc,
wid = id,
within = direction,
between = maingroup,
type = 3
)["ANOVA"]Data is unbalanced (unequal N per group). Make sure you specified a well-considered value for the type argument to ezANOVA().$ANOVANA- ANOVA with factor MainGroup, for Forward only.
# # Lexical Recall ANOVA 2
# acc_aov2 <- aov(acc ~ maingroup, data = filter(participant_data, direction == "forward"))
# summary(acc_aov2)
# acc_lsd2 <- LSD.test(acc_aov2, "maingroup", group = FALSE)
# acc_lsd2$comparison
ezANOVA(
data = filter(participant_data, direction == "forward"),
dv = acc,
wid = id,
between = maingroup,
type = 3
)["ANOVA"]Data is unbalanced (unequal N per group). Make sure you specified a well-considered value for the type argument to ezANOVA().$ANOVANA- ANOVA with factor MainGroup, for Reverse only.
# # Lexical Recall ANOVA 3
# acc_aov3 <- aov(acc ~ maingroup, data = filter(participant_data, direction == "reversed"))
# summary(acc_aov3)
# acc_lsd3 <- LSD.test(acc_aov3, "maingroup", group = FALSE)
# acc_lsd3$comparison
ezANOVA(
data = filter(participant_data, direction == "reversed"),
dv = acc,
wid = id,
between = maingroup,
type = 3
)["ANOVA"]Data is unbalanced (unequal N per group). Make sure you specified a well-considered value for the type argument to ezANOVA().$ANOVANAAoA Correlations
Next, we want to look at correlations between AoA and Gist, and betwen AoA and Lexical Recall. Rain asked for forward and reversed separately (1) deaf only, (2) hearing only, and (3) both. Let’s make it work.
# Let's make participant-level data, and have forward/reversed in separate columns
lexgist_data <- fulldata %>%
group_by(maingroup, participant, direction) %>%
mutate(gist = mean(gist, na.rm = TRUE),
lex = mean(acc, na.rm = TRUE)) %>%
ungroup() %>%
select(maingroup, participant, hearing, direction, aoasl, signyrs, age, gist, lex) %>%
distinct() %>%
gather(metric, value, gist:lex) %>%
unite(metricvalue, c(metric, direction), sep = "_") %>%
spread(metricvalue, value) %>%
select(-participant, -maingroup)
lexgist_deaf <- lexgist_data %>% filter(hearing == "Deaf") %>% select(-hearing)
lexgist_hearing <- lexgist_data %>% filter(hearing == "Hearing") %>% select(-hearing)
lexgist_all <- lexgist_data %>% select(-hearing)
# Load awesome function to make correlation tables with stars for significance
# From: https://myowelt.blogspot.co.uk/2008/04/beautiful-correlation-tables-in-r.html
corstarsl <- function(x){
require(Hmisc)
x <- as.matrix(x)
R <- Hmisc::rcorr(x)$r
p <- Hmisc::rcorr(x)$P
## define notions for significance levels; spacing is important.
mystars <- ifelse(p < .001, "***", ifelse(p < .01, "** ", ifelse(p < .05, "* ", " ")))
## trunctuate the matrix that holds the correlations to two decimal
R <- format(round(cbind(rep(-1.11, ncol(x)), R), 2))[,-1]
## build a new matrix that includes the correlations with their apropriate stars
Rnew <- matrix(paste(R, mystars, sep=""), ncol=ncol(x))
diag(Rnew) <- paste(diag(R), " ", sep="")
rownames(Rnew) <- colnames(x)
colnames(Rnew) <- paste(colnames(x), "", sep="")
## remove upper triangle
Rnew <- as.matrix(Rnew)
Rnew[upper.tri(Rnew, diag = TRUE)] <- ""
Rnew <- as.data.frame(Rnew)
## remove last column and return the matrix (which is now a data frame)
Rnew <- cbind(Rnew[1:length(Rnew)-1])
return(Rnew)
}
# Correlations for Deaf
print("DEAF Correlations - Pearson's r")[1] "DEAF Correlations - Pearson's r"#corstarsl(lexgist_deaf)
Hmisc::rcorr(as.matrix(lexgist_deaf))$r aoasl signyrs age gist_forward gist_reversed lex_forward lex_reversed
aoasl 1.00000000 -0.50188273 0.2569266 0.08266371 -0.25585943 0.14743346 -0.25601873
signyrs -0.50188273 1.00000000 0.7005996 0.18321127 -0.01264108 0.27968505 0.06666998
age 0.25692660 0.70059955 1.0000000 0.27454460 -0.17264941 0.42997837 -0.12765634
gist_forward 0.08266371 0.18321127 0.2745446 1.00000000 0.01762269 -0.08155560 -0.09770666
gist_reversed -0.25585943 -0.01264108 -0.1726494 0.01762269 1.00000000 0.02667231 0.36021802
lex_forward 0.14743346 0.27968505 0.4299784 -0.08155560 0.02667231 1.00000000 0.35984892
lex_reversed -0.25601873 0.06666998 -0.1276563 -0.09770666 0.36021802 0.35984892 1.00000000print("DEAF Correlations - P-values")[1] "DEAF Correlations - P-values"Hmisc::rcorr(as.matrix(lexgist_deaf))$P aoasl signyrs age gist_forward gist_reversed lex_forward lex_reversed
aoasl NA 5.536857e-03 1.784812e-01 0.6698839 0.18035489 0.44533530 0.18007434
signyrs 0.005536857 NA 2.316796e-05 0.3414473 0.94810844 0.14172236 0.73113301
age 0.178481213 2.316796e-05 NA 0.1495001 0.37046625 0.01990877 0.50930544
gist_forward 0.669883883 3.414473e-01 1.495001e-01 NA 0.92770438 0.67406661 0.61409897
gist_reversed 0.180354888 9.481084e-01 3.704662e-01 0.9277044 NA 0.89076140 0.05492031
lex_forward 0.445335297 1.417224e-01 1.990877e-02 0.6740666 0.89076140 NA 0.05518766
lex_reversed 0.180074336 7.311330e-01 5.093054e-01 0.6140990 0.05492031 0.05518766 NAcat(paste("","\n",""))# Correlations for Hearing
print("HEARING Correlations - Pearson's r")[1] "HEARING Correlations - Pearson's r"#corstarsl(lexgist_hearing)
Hmisc::rcorr(as.matrix(lexgist_hearing))$r aoasl signyrs age gist_forward gist_reversed lex_forward lex_reversed
aoasl 1.00000000 -0.07887013 0.3468184 -0.15525565 0.07815751 0.02500269 0.01411303
signyrs -0.07887013 1.00000000 0.9021947 0.57814670 0.28845748 0.36725658 0.20828810
age 0.34681845 0.90219468 1.0000000 0.44651473 0.30963454 0.30931753 0.20139593
gist_forward -0.15525565 0.57814670 0.4465147 1.00000000 0.29502174 0.57154566 0.07645807
gist_reversed 0.07815751 0.28845748 0.3096345 0.29502174 1.00000000 0.35682374 0.57951176
lex_forward 0.02500269 0.36725658 0.3093175 0.57154566 0.35682374 1.00000000 0.36558339
lex_reversed 0.01411303 0.20828810 0.2013959 0.07645807 0.57951176 0.36558339 1.00000000print("HEARING Correlations - P-values")[1] "HEARING Correlations - P-values"Hmisc::rcorr(as.matrix(lexgist_hearing))$P aoasl signyrs age gist_forward gist_reversed lex_forward lex_reversed
aoasl NA 7.205586e-01 1.049514e-01 0.479339909 0.722986497 0.909841021 0.949040176
signyrs 0.7205586 NA 4.046899e-09 0.003857505 0.181932462 0.084723426 0.340222805
age 0.1049514 4.046899e-09 NA 0.032691671 0.150502508 0.150942632 0.356794880
gist_forward 0.4793399 3.857505e-03 3.269167e-02 NA 0.171744860 0.004385475 0.728786878
gist_reversed 0.7229865 1.819325e-01 1.505025e-01 0.171744860 NA 0.094647552 0.003755275
lex_forward 0.9098410 8.472343e-02 1.509426e-01 0.004385475 0.094647552 NA 0.086260140
lex_reversed 0.9490402 3.402228e-01 3.567949e-01 0.728786878 0.003755275 0.086260140 NAcat(paste("","\n",""))# Correlations for All
print("ALL Correlations - Pearson's r")[1] "ALL Correlations - Pearson's r"#corstarsl(lexgist_all)
Hmisc::rcorr(as.matrix(lexgist_all))$r aoasl signyrs age gist_forward gist_reversed lex_forward lex_reversed
aoasl 1.00000000 -0.7852245 -0.3136458 -0.3230903 -0.3922803 -0.08115845 -0.3394521
signyrs -0.78522450 1.0000000 0.8314757 0.4956183 0.3356193 0.29698992 0.3182316
age -0.31364575 0.8314757 1.0000000 0.4602616 0.1930257 0.36318284 0.1886432
gist_forward -0.32309031 0.4956183 0.4602616 1.0000000 0.2712761 0.49279109 0.1521110
gist_reversed -0.39228034 0.3356193 0.1930257 0.2712761 1.0000000 0.21701819 0.4921550
lex_forward -0.08115845 0.2969899 0.3631828 0.4927911 0.2170182 1.00000000 0.3800772
lex_reversed -0.33945209 0.3182316 0.1886432 0.1521110 0.4921550 0.38007715 1.0000000print("ALL Correlations - P-values")[1] "ALL Correlations - P-values"Hmisc::rcorr(as.matrix(lexgist_all))$P aoasl signyrs age gist_forward gist_reversed lex_forward lex_reversed
aoasl NA 5.523138e-12 2.356068e-02 0.0194786567 0.0040239673 0.5673489939 0.0138201778
signyrs 5.523138e-12 NA 2.309264e-14 0.0001870211 0.0150006737 0.0325116001 0.0214967644
age 2.356068e-02 2.309264e-14 NA 0.0005965582 0.1703671221 0.0081373882 0.1804672184
gist_forward 1.947866e-02 1.870211e-04 5.965582e-04 NA 0.0517394597 0.0002061625 0.2817023354
gist_reversed 4.023967e-03 1.500067e-02 1.703671e-01 0.0517394597 NA 0.1222542968 0.0002107074
lex_forward 5.673490e-01 3.251160e-02 8.137388e-03 0.0002061625 0.1222542968 NA 0.0054478274
lex_reversed 1.382018e-02 2.149676e-02 1.804672e-01 0.2817023354 0.0002107074 0.0054478274 NAI’m also including nicely formatted tables with *** indicators of significance for quick referencing. Order: Deaf, Hearing, All.
corstarsl(lexgist_deaf)corstarsl(lexgist_hearing)corstarsl(lexgist_all)Scatterplot of Correlations
Let’s visualize what’s happening with the correlations here.
ggpairs(lexgist_data, columns = c(2:8), aes(color = hearing))
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Eye Gaze Data
Now eye gaze data. Boxplots first. Also here, we’re renaming “chin” to “neck” because that’s what it actually is! But we also have to fix all NA’s in the percentages to zeros, becuase that’s what they actually are.
# rename chin to neck
fulldata <- fulldata %>%
rename(neck = chin) %>%
gather(aoi, percent, belly:upperchest)
# Fix all NA's in Percent column to 0
fixpercent <- fulldata$percent
fulldata$percent <- coalesce(fixpercent, 0)
fulldata <- fulldata %>%
spread(aoi, percent)
fulldata %>%
filter(eye_exclude == FALSE) %>%
select(direction, belly:upperchest) %>%
gather(aoi, percent, belly:upperchest) %>%
ggplot(aes(x = aoi, y = percent, fill = direction)) + geom_boxplot()
But let’s try error charts too! Instead of boxplots.
fulldata_error <- fulldata %>%
filter(eye_exclude == FALSE) %>%
gather(aoi, percent, belly:upperchest) %>%
group_by(id, direction, aoi) %>%
summarise(percent = mean(percent, na.rm = TRUE)) %>%
ungroup() %>%
distinct() %>%
group_by(direction, aoi) %>%
summarise(mean = mean(percent, na.rm = TRUE),
sd = sd(percent, na.rm = TRUE),
count = n(),
se = sd/sqrt(count))
fulldata_error$aoi <- fct_relevel(fulldata_error$aoi, c("forehead","eyes","mouth","neck","upperchest",
"midchest","lowerchest","belly","left","right"))
fulldata_error %>%
ggplot(aes(x = aoi, y = mean, fill = direction)) +
geom_bar(stat = "identity", position = position_dodge()) +
geom_errorbar(aes(ymin = mean-se, ymax = mean+se), position = position_dodge(0.9), width = 0.5) +
labs(title = "Eye Gaze Behavior", x = "", y = "looking time") +
scale_y_continuous(labels = percent, limits = c(0,.70)) +
theme_bw() +
theme(axis.text.x = element_text(angle = 30, hjust = 1, vjust = 1))
And a table of eye gaze results too
fulldata_gazetable <- fulldata %>%
filter(eye_exclude == FALSE) %>%
gather(aoi, percent, belly:upperchest) %>%
group_by(id, maingroup, direction, aoi) %>%
summarise(percent = mean(percent, na.rm = TRUE)) %>%
ungroup() %>%
distinct() %>%
group_by(maingroup, direction, aoi) %>%
summarise(mean = mean(percent, na.rm = TRUE),
sd = sd(percent, na.rm = TRUE)) %>%
mutate(mean = round(mean*100,1),
sd = round(sd*100,1)) %>%
mutate(value = paste(mean, sd, sep = " ± ")) %>%
mutate(value = paste(value, "%", sep = ""))
fulldata_gazetable$aoi <- fct_relevel(fulldata_gazetable$aoi, c("forehead","eyes","mouth","neck","upperchest",
"midchest","lowerchest","belly","left","right"))
fulldata_gazetable %>%
ungroup() %>%
select(-mean, -sd) %>%
spread(aoi, value)fulldata_total <- fulldata %>%
filter(eye_exclude == FALSE) %>%
gather(aoi, percent, belly:upperchest) %>%
group_by(aoi) %>%
summarise(percent = mean(percent, na.rm = TRUE)) %>%
spread(aoi, percent)
#sum(fulldata_total$eyes, fulldata_total$mouth, fulldata_total$neck)
#fulldata_total$left
#fulldata_total$rightBig Three-Way ANOVA
Now we’re going to try a three-way Group x Direction x AOI ANOVA with the top 3 AOIs (Eyes, Mouth, Neck)
$ANOVA
Effect DFn DFd F p p<.05 ges
2 maingroup 3 47 3.0877532 3.602912e-02 * 0.0019613548
3 direction 1 47 14.6917045 3.751848e-04 * 0.0008045246
5 aoi 2 94 38.9261560 4.838451e-13 * 0.4115853001
4 maingroup:direction 3 47 0.8509636 4.731139e-01 0.0001398905
6 maingroup:aoi 6 94 1.1224334 3.553342e-01 0.0570561521
7 direction:aoi 2 94 7.0028846 1.462185e-03 * 0.0208462714
8 maingroup:direction:aoi 6 94 0.6318533 7.044042e-01 0.0057298396Left Vs Right
$ANOVA
Effect DFn DFd F p p<.05 ges
2 maingroup 3 47 2.5418616 0.06755936 0.09343220
3 aoi 1 47 1.8880127 0.17594392 0.01444195
4 maingroup:aoi 3 47 0.8434728 0.47700559 0.01926124Interactions Visualization
So we have significant maingroup:aoi and direction:aoi interactions. Let’s try to visualize what can be driving these. We can go back to the SEM chart but break it down…
First are the maingroup:aoi charts The error bars are not 100% accurate, I took a quick-n-easy way around
aoi3_interactions_maingroupaoi <- fulldata %>%
filter(eye_exclude == FALSE) %>%
select(id, participant, maingroup, direction, eyes, mouth, neck) %>%
gather(aoi, percent, c(eyes, mouth, neck)) %>%
group_by(id, maingroup, direction, aoi) %>%
mutate(percent = mean(percent, na.rm = TRUE)) %>%
distinct() %>%
group_by(maingroup, aoi) %>%
summarise(mean = mean(percent, na.rm = TRUE),
sd = sd(percent, na.rm = TRUE),
count = n()/2,
se = sd/sqrt(count))
# I need to first collapse across stories for each participant...here I didn't. Must fix later.
aoi3_interactions_maingroupaoi %>%
ggplot(aes(x = aoi, y = mean, fill = maingroup)) +
geom_bar(stat = "identity", position = position_dodge()) +
geom_errorbar(aes(ymin = mean-se, ymax = mean+se), position = position_dodge(0.9), width = 0.5) +
labs(title = "MainGroup & AOI Interaction 1", subtitle = "Error bars represent SE", x = "", y = "percent looking") +
scale_y_continuous(labels = percent)
aoi3_interactions_maingroupaoi %>%
ggplot(aes(x = maingroup, y = mean, fill = aoi)) +
geom_bar(stat = "identity", position = position_dodge()) +
geom_errorbar(aes(ymin = mean-se, ymax = mean+se), position = position_dodge(0.9), width = 0.5) +
labs(title = "MainGroup & AOI Interaction 2", subtitle = "Error bars represent SE", x = "", y = "percent looking") +
scale_y_continuous(labels = percent)
First are the direction:aoi charts The error bars are not 100% accurate, I took a quick-n-easy way around
aoi3_interactions_directionaoi <- fulldata %>%
filter(eye_exclude == FALSE) %>%
select(id, participant, maingroup, direction, eyes, mouth, neck) %>%
gather(aoi, percent, c(eyes, mouth, neck)) %>%
group_by(id, maingroup, direction, aoi) %>%
mutate(percent = mean(percent, na.rm = TRUE)) %>%
distinct() %>%
group_by(direction, aoi) %>%
summarise(mean = mean(percent, na.rm = TRUE),
sd = sd(percent, na.rm = TRUE),
count = n(),
se = sd/sqrt(count))
# I need to first collapse across stories for each participant...here I didn't. Must fix later.
aoi3_interactions_directionaoi %>%
ggplot(aes(x = aoi, y = mean, fill = direction)) +
geom_bar(stat = "identity", position = position_dodge()) +
geom_errorbar(aes(ymin = mean-se, ymax = mean+se), position = position_dodge(0.9), width = 0.5) +
labs(title = "MainGroup & Direction Interaction 1", subtitle = "Error bars represent SE", x = "", y = "percent looking") +
scale_y_continuous(labels = percent)
aoi3_interactions_directionaoi %>%
ggplot(aes(x = direction, y = mean, fill = aoi)) +
geom_bar(stat = "identity", position = position_dodge()) +
geom_errorbar(aes(ymin = mean-se, ymax = mean+se), position = position_dodge(0.9), width = 0.5) +
labs(title = "MainGroup & Direction Interaction 2", subtitle = "Error bars represent SE", x = "", y = "percent looking") +
scale_y_continuous(labels = percent)
All the AOI means
Let’s get tables of our means here, in various configurations
Eyes
- ANOVA with factors MainGroup & Direction.
Converting "id" to factor for ANOVA.Converting "direction" to factor for ANOVA.Converting "maingroup" to factor for ANOVA.Data is unbalanced (unequal N per group). Make sure you specified a well-considered value for the type argument to ezANOVA().$ANOVANA- ANOVA with factor MainGroup, for Forward only.
Converting "id" to factor for ANOVA.Converting "maingroup" to factor for ANOVA.Data is unbalanced (unequal N per group). Make sure you specified a well-considered value for the type argument to ezANOVA().$ANOVANA- ANOVA with factor MainGroup, for Reverse only.
Converting "id" to factor for ANOVA.Converting "maingroup" to factor for ANOVA.Data is unbalanced (unequal N per group). Make sure you specified a well-considered value for the type argument to ezANOVA().$ANOVANAMouth
- ANOVA with factors MainGroup & Direction.
Converting "id" to factor for ANOVA.Converting "direction" to factor for ANOVA.Converting "maingroup" to factor for ANOVA.Data is unbalanced (unequal N per group). Make sure you specified a well-considered value for the type argument to ezANOVA().$ANOVANA- ANOVA with factor MainGroup, for Forward only.
Converting "id" to factor for ANOVA.Converting "maingroup" to factor for ANOVA.Data is unbalanced (unequal N per group). Make sure you specified a well-considered value for the type argument to ezANOVA().$ANOVANA- ANOVA with factor MainGroup, for Reverse only.
Converting "id" to factor for ANOVA.Converting "maingroup" to factor for ANOVA.Data is unbalanced (unequal N per group). Make sure you specified a well-considered value for the type argument to ezANOVA().$ANOVANANeck
- ANOVA with factors MainGroup & Direction.
Converting "id" to factor for ANOVA.Converting "direction" to factor for ANOVA.Converting "maingroup" to factor for ANOVA.Data is unbalanced (unequal N per group). Make sure you specified a well-considered value for the type argument to ezANOVA().$ANOVANA- ANOVA with factor MainGroup, for Forward only.
Converting "id" to factor for ANOVA.Converting "maingroup" to factor for ANOVA.Data is unbalanced (unequal N per group). Make sure you specified a well-considered value for the type argument to ezANOVA().$ANOVANA- ANOVA with factor MainGroup, for Reverse only.
Converting "id" to factor for ANOVA.Converting "maingroup" to factor for ANOVA.Data is unbalanced (unequal N per group). Make sure you specified a well-considered value for the type argument to ezANOVA().$ANOVANAFaceChest
We originally defined FaceChest such that
- Face = eyes + mouth + chin
- Chest = upperchest + midchest + lowerchest
BUT. Chin is actually neck. It’s not even part of the face if you think about it. So I’m redefining FaceChest as:
- Face = forehead + eyes + mouth
- Chest = neck + upperchest + midchest + lowerchest
So let’s do this. Then see what’s happening across groups for FaceChest.
Cool. Next we’ll do error bar charts using the new FaceChest across groups.
facechest_info <- fulldata %>%
filter(eye_exclude == FALSE) %>%
group_by(maingroup, direction, participant) %>%
summarise(facechest = mean(facechest, na.rm = TRUE)) %>%
group_by(maingroup, direction) %>%
summarise(mean = mean(facechest),
sd = sd(facechest),
n = n(),
se = sd/sqrt(n)) %>%
ungroup() %>%
mutate(maingroup = case_when(
maingroup == "DeafEarly" ~ "Deaf Early",
maingroup == "DeafLate" ~ "Deaf Late",
maingroup == "HearingLate" ~ "Hearing Late",
maingroup == "HearingNovice" ~ "Hearing Novice"
))
ggplot(facechest_info, aes(x = maingroup, y = mean, fill = direction, color = direction)) +
geom_point(stat = "identity", position = position_dodge(0.5), size = 2) +
geom_errorbar(aes(ymin = mean-se, ymax = mean+se), position = position_dodge(0.5), width = 0.3, size = 1) +
labs(title = "Face-Chest Ratio", x = "", y = "face-chest ratio") +
scale_y_continuous(limits = c(-1,1)) +
geom_hline(yintercept = 0, linetype = "dotted") +
theme_bw()
Now let’s do the ANOVAs. Also skipping LSDs here.
- ANOVA with factors MainGroup & Direction.
Grouping rowwise data frame strips rowwise natureConverting "id" to factor for ANOVA.Converting "direction" to factor for ANOVA.Converting "maingroup" to factor for ANOVA.Data is unbalanced (unequal N per group). Make sure you specified a well-considered value for the type argument to ezANOVA().$ANOVANA- ANOVA with factor MainGroup, for Forward only.
Converting "id" to factor for ANOVA.Converting "maingroup" to factor for ANOVA.Data is unbalanced (unequal N per group). Make sure you specified a well-considered value for the type argument to ezANOVA().$ANOVANA- ANOVA with factor MainGroup, for Reverse only.
Converting "id" to factor for ANOVA.Converting "maingroup" to factor for ANOVA.Data is unbalanced (unequal N per group). Make sure you specified a well-considered value for the type argument to ezANOVA().$ANOVANAEye Gaze & Performance Correlations
Next we’re going to correlate our eye gaze metrics (Eye, Mouth, Neck, and FaceChest) with lexical recall and gist. Okay! But remember we have a slightly smaller dataset here because we’ve excluded some participants for having bad eye data (but they had valid behavioral data so we kept them in the AoA-Performance correlations above).
IMPORTANT: We also removed gist_forward column.
eyeperf <- fulldata %>%
filter(eye_exclude == FALSE) %>%
select(participant, maingroup, hearing, direction, aoasl, signyrs, selfrate, acc, gist, eyes, mouth, neck, facechest) %>%
group_by(maingroup, participant, direction) %>%
mutate(gist = mean(gist, na.rm = TRUE),
lex = mean(acc, na.rm = TRUE),
eyes = mean(eyes, na.rm = TRUE),
mouth = mean(mouth, na.rm = TRUE),
neck = mean(neck, na.rm = TRUE),
facechest = mean(facechest, na.rm = TRUE)) %>%
ungroup() %>%
select(maingroup, participant, hearing, direction, aoasl, signyrs, selfrate, gist, lex, eyes, mouth, neck, facechest) %>%
distinct() %>%
gather(metric, value, gist:facechest) %>%
unite(metricvalue, c(metric, direction), sep = "_") %>%
spread(metricvalue, value) %>%
select(-participant, -maingroup) %>%
select(hearing, aoasl, signyrs, selfrate, gist_reversed, lex_forward, lex_reversed, eyes_forward,
eyes_reversed, mouth_forward, mouth_reversed, neck_forward, neck_reversed,
facechest_forward, facechest_reversed)Grouping rowwise data frame strips rowwise natureeyeperf_deaf <- eyeperf %>% filter(hearing == "Deaf") %>% select(-hearing)
eyeperf_deaf_fw <- eyeperf_deaf %>% select(aoasl, signyrs, selfrate, lex_forward, eyes_forward,
mouth_forward, neck_forward, facechest_forward)
eyeperf_deaf_rv <- eyeperf_deaf %>% select(aoasl, signyrs, selfrate, gist_reversed, lex_reversed,
eyes_reversed, mouth_reversed, neck_reversed, facechest_reversed)
eyeperf_hearing <- eyeperf %>% filter(hearing == "Hearing") %>% select(-hearing)
eyeperf_hearing_fw <- eyeperf_hearing %>% select(aoasl, signyrs, selfrate, lex_forward, eyes_forward,
mouth_forward, neck_forward, facechest_forward)
eyeperf_hearing_rv <- eyeperf_hearing %>% select(aoasl, signyrs, selfrate, gist_reversed, lex_reversed,
eyes_reversed, mouth_reversed, neck_reversed, facechest_reversed)
eyeperf_all <- eyeperf %>% select(-hearing)
eyeperf_all_fw <- eyeperf_all %>% select(aoasl, signyrs, selfrate, lex_forward, eyes_forward,
mouth_forward, neck_forward, facechest_forward)
eyeperf_all_rv <- eyeperf_all %>% select(aoasl, signyrs, selfrate, gist_reversed, lex_reversed,
eyes_reversed, mouth_reversed, neck_reversed, facechest_reversed)Deaf Correlations, Forward
# Correlations for Deaf
print("DEAF Correlations - Pearson's r")[1] "DEAF Correlations - Pearson's r"#corstarsl(lexgist_deaf)
Hmisc::rcorr(as.matrix(eyeperf_deaf_fw))$r aoasl signyrs selfrate lex_forward eyes_forward mouth_forward neck_forward
aoasl 1.00000000 -0.50188273 NaN 0.1172403 0.06486298 0.17733160 -0.2393697
signyrs -0.50188273 1.00000000 NaN 0.3851766 0.08415831 0.09383579 -0.1334632
selfrate NaN NaN 1 NaN NaN NaN NaN
lex_forward 0.11724032 0.38517660 NaN 1.0000000 0.19560269 0.11273646 -0.2818219
eyes_forward 0.06486298 0.08415831 NaN 0.1956027 1.00000000 -0.40389580 -0.4021353
mouth_forward 0.17733160 0.09383579 NaN 0.1127365 -0.40389580 1.00000000 -0.6652489
neck_forward -0.23936971 -0.13346320 NaN -0.2818219 -0.40213531 -0.66524893 1.0000000
facechest_forward 0.25001433 0.15009922 NaN 0.2796232 0.37868726 0.69180554 -0.9914181
facechest_forward
aoasl 0.2500143
signyrs 0.1500992
selfrate NaN
lex_forward 0.2796232
eyes_forward 0.3786873
mouth_forward 0.6918055
neck_forward -0.9914181
facechest_forward 1.0000000print("DEAF Correlations - P-values")[1] "DEAF Correlations - P-values"Hmisc::rcorr(as.matrix(eyeperf_deaf_fw))$P aoasl signyrs selfrate lex_forward eyes_forward mouth_forward neck_forward
aoasl NA 0.005536857 NaN 0.54472632 0.73816416 3.574282e-01 2.110728e-01
signyrs 0.005536857 NA NaN 0.03907663 0.66425759 6.282694e-01 4.900657e-01
selfrate NaN NaN NA NaN NaN NaN NaN
lex_forward 0.544726321 0.039076627 NaN NA 0.30920849 5.603910e-01 1.385770e-01
eyes_forward 0.738164157 0.664257588 NaN 0.30920849 NA 2.978758e-02 3.057654e-02
mouth_forward 0.357428170 0.628269399 NaN 0.56039098 0.02978758 NA 8.235941e-05
neck_forward 0.211072759 0.490065680 NaN 0.13857704 0.03057654 8.235941e-05 NA
facechest_forward 0.190862018 0.437057268 NaN 0.14181424 0.04279116 3.228725e-05 0.000000e+00
facechest_forward
aoasl 1.908620e-01
signyrs 4.370573e-01
selfrate NaN
lex_forward 1.418142e-01
eyes_forward 4.279116e-02
mouth_forward 3.228725e-05
neck_forward 0.000000e+00
facechest_forward NAcat(paste("","\n",""))corstarsl(eyeperf_deaf_fw)Deaf Correlations, Reversed
# Correlations for Deaf
print("DEAF Correlations - Pearson's r")[1] "DEAF Correlations - Pearson's r"#corstarsl(lexgist_deaf)
Hmisc::rcorr(as.matrix(eyeperf_deaf_rv))$r aoasl signyrs selfrate gist_reversed lex_reversed eyes_reversed
aoasl 1.00000000 -0.501882732 NaN -0.22660857 -0.16780065 0.02247978
signyrs -0.50188273 1.000000000 NaN -0.10665206 -0.02521074 0.09583659
selfrate NaN NaN 1 NaN NaN NaN
gist_reversed -0.22660857 -0.106652059 NaN 1.00000000 0.30316901 0.02254098
lex_reversed -0.16780065 -0.025210742 NaN 0.30316901 1.00000000 -0.07989090
eyes_reversed 0.02247978 0.095836595 NaN 0.02254098 -0.07989090 1.00000000
mouth_reversed 0.30344567 0.007193097 NaN 0.06590948 0.11828493 -0.45147687
neck_reversed -0.32970390 -0.052028485 NaN -0.07553297 -0.01236971 -0.42235607
facechest_reversed 0.30789807 0.086108170 NaN 0.08644924 0.03749677 0.43328831
mouth_reversed neck_reversed facechest_reversed
aoasl 0.303445667 -0.32970390 0.30789807
signyrs 0.007193097 -0.05202848 0.08610817
selfrate NaN NaN NaN
gist_reversed 0.065909483 -0.07553297 0.08644924
lex_reversed 0.118284926 -0.01236971 0.03749677
eyes_reversed -0.451476872 -0.42235607 0.43328831
mouth_reversed 1.000000000 -0.59771758 0.59842563
neck_reversed -0.597717583 1.00000000 -0.99196124
facechest_reversed 0.598425627 -0.99196124 1.00000000print("DEAF Correlations - P-values")[1] "DEAF Correlations - P-values"Hmisc::rcorr(as.matrix(eyeperf_deaf_rv))$P aoasl signyrs selfrate gist_reversed lex_reversed eyes_reversed
aoasl NA 0.005536857 NaN 0.2462148 0.3933760 0.90960039
signyrs 0.005536857 NA NaN 0.5890836 0.8986709 0.62759257
selfrate NaN NaN NA NaN NaN NaN
gist_reversed 0.246214823 0.589083649 NaN NA 0.1168234 0.90935525
lex_reversed 0.393375956 0.898670879 NaN 0.1168234 NA 0.68612731
eyes_reversed 0.909600391 0.627592567 NaN 0.9093553 0.6861273 NA
mouth_reversed 0.116473224 0.971021367 NaN 0.7389667 0.5488533 0.01588232
neck_reversed 0.086644292 0.792600787 NaN 0.7024562 0.9501866 0.02515937
facechest_reversed 0.110944251 0.663069865 NaN 0.6618134 0.8497523 0.02126289
mouth_reversed neck_reversed facechest_reversed
aoasl 0.1164732242 0.0866442923 0.1109442511
signyrs 0.9710213673 0.7926007873 0.6630698653
selfrate NaN NaN NaN
gist_reversed 0.7389666674 0.7024561767 0.6618134031
lex_reversed 0.5488532932 0.9501866460 0.8497523419
eyes_reversed 0.0158823163 0.0251593680 0.0212628913
mouth_reversed NA 0.0007826592 0.0007685904
neck_reversed 0.0007826592 NA 0.0000000000
facechest_reversed 0.0007685904 0.0000000000 NAcat(paste("","\n",""))corstarsl(eyeperf_deaf_rv)Deaf Correlations, Both Directions
# Correlations for Deaf
print("DEAF Correlations - Pearson's r")[1] "DEAF Correlations - Pearson's r"#corstarsl(lexgist_deaf)
Hmisc::rcorr(as.matrix(eyeperf_deaf))$r aoasl signyrs selfrate gist_reversed lex_forward lex_reversed eyes_forward
aoasl 1.00000000 -0.501882732 NaN -0.22660857 0.1172403 -0.16780065 0.06486298
signyrs -0.50188273 1.000000000 NaN -0.10665206 0.3851766 -0.02521074 0.08415831
selfrate NaN NaN 1 NaN NaN NaN NaN
gist_reversed -0.22660857 -0.106652059 NaN 1.00000000 -0.1103272 0.30316901 -0.06319147
lex_forward 0.11724032 0.385176599 NaN -0.11032724 1.0000000 0.12103733 0.19560269
lex_reversed -0.16780065 -0.025210742 NaN 0.30316901 0.1210373 1.00000000 -0.09834590
eyes_forward 0.06486298 0.084158309 NaN -0.06319147 0.1956027 -0.09834590 1.00000000
eyes_reversed 0.02247978 0.095836595 NaN 0.02254098 0.1946094 -0.07989090 0.53012073
mouth_forward 0.17733160 0.093835786 NaN 0.13998124 0.1127365 0.13984634 -0.40389580
mouth_reversed 0.30344567 0.007193097 NaN 0.06590948 0.1511229 0.11828493 -0.18798503
neck_forward -0.23936971 -0.133463204 NaN -0.10845388 -0.2818219 -0.06254636 -0.40213531
neck_reversed -0.32970390 -0.052028485 NaN -0.07553297 -0.2951812 -0.01236971 -0.29756692
facechest_forward 0.25001433 0.150099218 NaN 0.10233361 0.2796232 0.05057043 0.37868726
facechest_reversed 0.30789807 0.086108170 NaN 0.08644924 0.3039166 0.03749677 0.28118142
eyes_reversed mouth_forward mouth_reversed neck_forward neck_reversed
aoasl 0.02247978 0.17733160 0.303445667 -0.23936971 -0.32970390
signyrs 0.09583659 0.09383579 0.007193097 -0.13346320 -0.05202848
selfrate NaN NaN NaN NaN NaN
gist_reversed 0.02254098 0.13998124 0.065909483 -0.10845388 -0.07553297
lex_forward 0.19460939 0.11273646 0.151122898 -0.28182188 -0.29518121
lex_reversed -0.07989090 0.13984634 0.118284926 -0.06254636 -0.01236971
eyes_forward 0.53012073 -0.40389580 -0.187985033 -0.40213531 -0.29756692
eyes_reversed 1.00000000 -0.10849377 -0.451476872 -0.32258892 -0.42235607
mouth_forward -0.10849377 1.00000000 0.648739517 -0.66524893 -0.54832852
mouth_reversed -0.45147687 0.64873952 1.000000000 -0.52286172 -0.59771758
neck_forward -0.32258892 -0.66524893 -0.522861719 1.00000000 0.82565296
neck_reversed -0.42235607 -0.54832852 -0.597717583 0.82565296 1.00000000
facechest_forward 0.32650983 0.69180554 0.496383160 -0.99141806 -0.79461950
facechest_reversed 0.43328831 0.58112735 0.598425627 -0.83648926 -0.99196124
facechest_forward facechest_reversed
aoasl 0.25001433 0.30789807
signyrs 0.15009922 0.08610817
selfrate NaN NaN
gist_reversed 0.10233361 0.08644924
lex_forward 0.27962315 0.30391663
lex_reversed 0.05057043 0.03749677
eyes_forward 0.37868726 0.28118142
eyes_reversed 0.32650983 0.43328831
mouth_forward 0.69180554 0.58112735
mouth_reversed 0.49638316 0.59842563
neck_forward -0.99141806 -0.83648926
neck_reversed -0.79461950 -0.99196124
facechest_forward 1.00000000 0.81218421
facechest_reversed 0.81218421 1.00000000print("DEAF Correlations - P-values")[1] "DEAF Correlations - P-values"Hmisc::rcorr(as.matrix(eyeperf_deaf))$P aoasl signyrs selfrate gist_reversed lex_forward lex_reversed eyes_forward
aoasl NA 0.005536857 NaN 0.2462148 0.54472632 0.3933760 0.738164157
signyrs 0.005536857 NA NaN 0.5890836 0.03907663 0.8986709 0.664257588
selfrate NaN NaN NA NaN NaN NaN NaN
gist_reversed 0.246214823 0.589083649 NaN NA 0.57623604 0.1168234 0.749383585
lex_forward 0.544726321 0.039076627 NaN 0.5762360 NA 0.5395250 0.309208485
lex_reversed 0.393375956 0.898670879 NaN 0.1168234 0.53952504 NA 0.618567671
eyes_forward 0.738164157 0.664257588 NaN 0.7493836 0.30920849 0.6185677 NA
eyes_reversed 0.909600391 0.627592567 NaN 0.9093553 0.32102372 0.6861273 0.003712324
mouth_forward 0.357428170 0.628269399 NaN 0.4774249 0.56039098 0.4778537 0.029787577
mouth_reversed 0.116473224 0.971021367 NaN 0.7389667 0.44270508 0.5488533 0.338088512
neck_forward 0.211072759 0.490065680 NaN 0.5827693 0.13857704 0.7518624 0.030576543
neck_reversed 0.086644292 0.792600787 NaN 0.7024562 0.12727155 0.9501866 0.124081940
facechest_forward 0.190862018 0.437057268 NaN 0.6043367 0.14181424 0.7982931 0.042791159
facechest_reversed 0.110944251 0.663069865 NaN 0.6618134 0.11587894 0.8497523 0.147206817
eyes_reversed mouth_forward mouth_reversed neck_forward neck_reversed
aoasl 0.909600391 3.574282e-01 0.1164732242 2.110728e-01 8.664429e-02
signyrs 0.627592567 6.282694e-01 0.9710213673 4.900657e-01 7.926008e-01
selfrate NaN NaN NaN NaN NaN
gist_reversed 0.909355255 4.774249e-01 0.7389666674 5.827693e-01 7.024562e-01
lex_forward 0.321023718 5.603910e-01 0.4427050811 1.385770e-01 1.272716e-01
lex_reversed 0.686127314 4.778537e-01 0.5488532932 7.518624e-01 9.501866e-01
eyes_forward 0.003712324 2.978758e-02 0.3380885118 3.057654e-02 1.240819e-01
eyes_reversed NA 5.826299e-01 0.0158823163 9.408046e-02 2.515937e-02
mouth_forward 0.582629892 NA 0.0001883815 8.235941e-05 2.519354e-03
mouth_reversed 0.015882316 1.883815e-04 NA 4.307483e-03 7.826592e-04
neck_forward 0.094080464 8.235941e-05 0.0043074830 NA 6.358068e-08
neck_reversed 0.025159368 2.519354e-03 0.0007826592 6.358068e-08 NA
facechest_forward 0.089925263 3.228725e-05 0.0072161132 0.000000e+00 4.429714e-07
facechest_reversed 0.021262891 1.183228e-03 0.0007685904 2.946681e-08 0.000000e+00
facechest_forward facechest_reversed
aoasl 1.908620e-01 1.109443e-01
signyrs 4.370573e-01 6.630699e-01
selfrate NaN NaN
gist_reversed 6.043367e-01 6.618134e-01
lex_forward 1.418142e-01 1.158789e-01
lex_reversed 7.982931e-01 8.497523e-01
eyes_forward 4.279116e-02 1.472068e-01
eyes_reversed 8.992526e-02 2.126289e-02
mouth_forward 3.228725e-05 1.183228e-03
mouth_reversed 7.216113e-03 7.685904e-04
neck_forward 0.000000e+00 2.946681e-08
neck_reversed 4.429714e-07 0.000000e+00
facechest_forward NA 1.541998e-07
facechest_reversed 1.541998e-07 NAcat(paste("","\n",""))corstarsl(eyeperf_deaf)Hearing Correlations, Forward
# Correlations for Hearing
print("HEARING Correlations - Pearson's r")[1] "HEARING Correlations - Pearson's r"#corstarsl(lexgist_hearing)
Hmisc::rcorr(as.matrix(eyeperf_hearing_fw))$r aoasl signyrs selfrate lex_forward eyes_forward mouth_forward
aoasl 1.00000000 -0.07887013 0.013855614 0.08908092 -0.311357170 0.17806749
signyrs -0.07887013 1.00000000 0.738478541 0.26130641 0.132498235 0.06609090
selfrate 0.01385561 0.73847854 1.000000000 0.47076830 0.003272709 -0.03631108
lex_forward 0.08908092 0.26130641 0.470768303 1.00000000 -0.206412390 0.04471777
eyes_forward -0.31135717 0.13249823 0.003272709 -0.20641239 1.000000000 -0.75736409
mouth_forward 0.17806749 0.06609090 -0.036311083 0.04471777 -0.757364094 1.00000000
neck_forward 0.38414940 -0.24596132 0.135873660 0.36551589 -0.547841430 -0.08094060
facechest_forward -0.28903425 0.30186427 -0.017766366 -0.28030962 0.532841146 0.14471392
neck_forward facechest_forward
aoasl 0.3841494 -0.28903425
signyrs -0.2459613 0.30186427
selfrate 0.1358737 -0.01776637
lex_forward 0.3655159 -0.28030962
eyes_forward -0.5478414 0.53284115
mouth_forward -0.0809406 0.14471392
neck_forward 1.0000000 -0.94241619
facechest_forward -0.9424162 1.00000000print("HEARING Correlations - P-values")[1] "HEARING Correlations - P-values"Hmisc::rcorr(as.matrix(eyeperf_hearing_fw))$P aoasl signyrs selfrate lex_forward eyes_forward mouth_forward
aoasl NA 7.205586e-01 9.499685e-01 0.68606718 1.481267e-01 4.162838e-01
signyrs 0.72055862 NA 5.732124e-05 0.22845065 5.467283e-01 7.644726e-01
selfrate 0.94996853 5.732124e-05 NA 0.02337677 9.881757e-01 8.693499e-01
lex_forward 0.68606718 2.284506e-01 2.337677e-02 NA 3.446869e-01 8.394476e-01
eyes_forward 0.14812668 5.467283e-01 9.881757e-01 0.34468693 NA 2.859607e-05
mouth_forward 0.41628381 7.644726e-01 8.693499e-01 0.83944758 2.859607e-05 NA
neck_forward 0.07033578 2.579308e-01 5.364661e-01 0.08632257 6.807338e-03 7.135195e-01
facechest_forward 0.18102107 1.615537e-01 9.358722e-01 0.19514544 8.849713e-03 5.100229e-01
neck_forward facechest_forward
aoasl 7.033578e-02 1.810211e-01
signyrs 2.579308e-01 1.615537e-01
selfrate 5.364661e-01 9.358722e-01
lex_forward 8.632257e-02 1.951454e-01
eyes_forward 6.807338e-03 8.849713e-03
mouth_forward 7.135195e-01 5.100229e-01
neck_forward NA 1.861311e-11
facechest_forward 1.861311e-11 NAcat(paste("","\n",""))corstarsl(eyeperf_hearing_fw)Hearing Correlations, Reversed
# Correlations for Hearing
print("HEARING Correlations - Pearson's r")[1] "HEARING Correlations - Pearson's r"#corstarsl(lexgist_hearing)
Hmisc::rcorr(as.matrix(eyeperf_hearing_rv))$r aoasl signyrs selfrate gist_reversed lex_reversed eyes_reversed
aoasl 1.00000000 -0.07887013 0.01385561 0.078157514 0.06994864 -0.234574154
signyrs -0.07887013 1.00000000 0.73847854 0.288457483 0.22209269 0.306984037
selfrate 0.01385561 0.73847854 1.00000000 0.521440804 0.36876982 0.192505553
gist_reversed 0.07815751 0.28845748 0.52144080 1.000000000 0.61989301 -0.003896097
lex_reversed 0.06994864 0.22209269 0.36876982 0.619893014 1.00000000 -0.128554240
eyes_reversed -0.23457415 0.30698404 0.19250555 -0.003896097 -0.12855424 1.000000000
mouth_reversed 0.06256448 0.04730904 -0.11038394 -0.057827983 0.01769962 -0.608459473
neck_reversed 0.29446188 -0.38249552 -0.08287912 0.223993644 0.24863580 -0.667670012
facechest_reversed -0.20474470 0.43157929 0.15951976 -0.163039193 -0.19097045 0.602644563
mouth_reversed neck_reversed facechest_reversed
aoasl 0.06256448 0.29446188 -0.2047447
signyrs 0.04730904 -0.38249552 0.4315793
selfrate -0.11038394 -0.08287912 0.1595198
gist_reversed -0.05782798 0.22399364 -0.1630392
lex_reversed 0.01769962 0.24863580 -0.1909705
eyes_reversed -0.60845947 -0.66767001 0.6026446
mouth_reversed 1.00000000 -0.09060995 0.2372879
neck_reversed -0.09060995 1.00000000 -0.9351352
facechest_reversed 0.23728788 -0.93513525 1.0000000print("HEARING Correlations - P-values")[1] "HEARING Correlations - P-values"Hmisc::rcorr(as.matrix(eyeperf_hearing_rv))$P aoasl signyrs selfrate gist_reversed lex_reversed eyes_reversed
aoasl NA 7.205586e-01 9.499685e-01 0.722986497 0.751135086 0.2813176741
signyrs 0.7205586 NA 5.732124e-05 0.181932462 0.308433949 0.1542103716
selfrate 0.9499685 5.732124e-05 NA 0.010720157 0.083351359 0.3788529812
gist_reversed 0.7229865 1.819325e-01 1.072016e-02 NA 0.001604978 0.9859236392
lex_reversed 0.7511351 3.084339e-01 8.335136e-02 0.001604978 NA 0.5588319622
eyes_reversed 0.2813177 1.542104e-01 3.788530e-01 0.985923639 0.558831962 NA
mouth_reversed 0.7767217 8.302717e-01 6.160912e-01 0.793254811 0.936112687 0.0020651940
neck_reversed 0.1725980 7.165620e-02 7.069493e-01 0.304204139 0.252624987 0.0004996113
facechest_reversed 0.3486849 3.975328e-02 4.671999e-01 0.457300336 0.382738792 0.0023393015
mouth_reversed neck_reversed facechest_reversed
aoasl 0.776721725 1.725980e-01 3.486849e-01
signyrs 0.830271706 7.165620e-02 3.975328e-02
selfrate 0.616091213 7.069493e-01 4.671999e-01
gist_reversed 0.793254811 3.042041e-01 4.573003e-01
lex_reversed 0.936112687 2.526250e-01 3.827388e-01
eyes_reversed 0.002065194 4.996113e-04 2.339301e-03
mouth_reversed NA 6.809521e-01 2.756272e-01
neck_reversed 0.680952065 NA 6.290368e-11
facechest_reversed 0.275627154 6.290368e-11 NAcat(paste("","\n",""))corstarsl(eyeperf_hearing_rv)Hearing Correlations, Both Directions
# Correlations for Hearing
print("HEARING Correlations - Pearson's r")[1] "HEARING Correlations - Pearson's r"#corstarsl(lexgist_hearing)
Hmisc::rcorr(as.matrix(eyeperf_hearing))$r aoasl signyrs selfrate gist_reversed lex_forward lex_reversed
aoasl 1.00000000 -0.07887013 0.013855614 0.078157514 0.08908092 0.06994864
signyrs -0.07887013 1.00000000 0.738478541 0.288457483 0.26130641 0.22209269
selfrate 0.01385561 0.73847854 1.000000000 0.521440804 0.47076830 0.36876982
gist_reversed 0.07815751 0.28845748 0.521440804 1.000000000 0.32119471 0.61989301
lex_forward 0.08908092 0.26130641 0.470768303 0.321194708 1.00000000 0.23279549
lex_reversed 0.06994864 0.22209269 0.368769825 0.619893014 0.23279549 1.00000000
eyes_forward -0.31135717 0.13249823 0.003272709 -0.201726153 -0.20641239 -0.09364320
eyes_reversed -0.23457415 0.30698404 0.192505553 -0.003896097 -0.04361310 -0.12855424
mouth_forward 0.17806749 0.06609090 -0.036311083 -0.022039175 0.04471777 -0.12913127
mouth_reversed 0.06256448 0.04730904 -0.110383935 -0.057827983 -0.08355581 0.01769962
neck_forward 0.38414940 -0.24596132 0.135873660 0.367403537 0.36551589 0.36037686
neck_reversed 0.29446188 -0.38249552 -0.082879119 0.223993644 0.27462074 0.24863580
facechest_forward -0.28903425 0.30186427 -0.017766366 -0.340341955 -0.28030962 -0.29633671
facechest_reversed -0.20474470 0.43157929 0.159519762 -0.163039193 -0.15098999 -0.19097045
eyes_forward eyes_reversed mouth_forward mouth_reversed neck_forward neck_reversed
aoasl -0.311357170 -0.234574154 0.17806749 0.06256448 0.3841494 0.29446188
signyrs 0.132498235 0.306984037 0.06609090 0.04730904 -0.2459613 -0.38249552
selfrate 0.003272709 0.192505553 -0.03631108 -0.11038394 0.1358737 -0.08287912
gist_reversed -0.201726153 -0.003896097 -0.02203918 -0.05782798 0.3674035 0.22399364
lex_forward -0.206412390 -0.043613102 0.04471777 -0.08355581 0.3655159 0.27462074
lex_reversed -0.093643203 -0.128554240 -0.12913127 0.01769962 0.3603769 0.24863580
eyes_forward 1.000000000 0.794651926 -0.75736409 -0.49470326 -0.5478414 -0.56784350
eyes_reversed 0.794651926 1.000000000 -0.53289974 -0.60845947 -0.5244592 -0.66767001
mouth_forward -0.757364094 -0.532899737 1.00000000 0.73505580 -0.0809406 0.06470188
mouth_reversed -0.494703263 -0.608459473 0.73505580 1.00000000 -0.0758556 -0.09060995
neck_forward -0.547841430 -0.524459183 -0.08094060 -0.07585560 1.0000000 0.76188785
neck_reversed -0.567843497 -0.667670012 0.06470188 -0.09060995 0.7618878 1.00000000
facechest_forward 0.532841146 0.513289094 0.14471392 0.19576141 -0.9424162 -0.78331023
facechest_reversed 0.509840250 0.602644563 0.05395970 0.23728788 -0.7260022 -0.93513525
facechest_forward facechest_reversed
aoasl -0.28903425 -0.2047447
signyrs 0.30186427 0.4315793
selfrate -0.01776637 0.1595198
gist_reversed -0.34034196 -0.1630392
lex_forward -0.28030962 -0.1509900
lex_reversed -0.29633671 -0.1909705
eyes_forward 0.53284115 0.5098403
eyes_reversed 0.51328909 0.6026446
mouth_forward 0.14471392 0.0539597
mouth_reversed 0.19576141 0.2372879
neck_forward -0.94241619 -0.7260022
neck_reversed -0.78331023 -0.9351352
facechest_forward 1.00000000 0.8350726
facechest_reversed 0.83507264 1.0000000print("HEARING Correlations - P-values")[1] "HEARING Correlations - P-values"Hmisc::rcorr(as.matrix(eyeperf_hearing))$P aoasl signyrs selfrate gist_reversed lex_forward lex_reversed
aoasl NA 7.205586e-01 9.499685e-01 0.722986497 0.68606718 0.751135086
signyrs 0.72055862 NA 5.732124e-05 0.181932462 0.22845065 0.308433949
selfrate 0.94996853 5.732124e-05 NA 0.010720157 0.02337677 0.083351359
gist_reversed 0.72298650 1.819325e-01 1.072016e-02 NA 0.13506603 0.001604978
lex_forward 0.68606718 2.284506e-01 2.337677e-02 0.135066028 NA 0.285087190
lex_reversed 0.75113509 3.084339e-01 8.335136e-02 0.001604978 0.28508719 NA
eyes_forward 0.14812668 5.467283e-01 9.881757e-01 0.355990294 0.34468693 0.670845077
eyes_reversed 0.28131767 1.542104e-01 3.788530e-01 0.985923639 0.84336552 0.558831962
mouth_forward 0.41628381 7.644726e-01 8.693499e-01 0.920492311 0.83944758 0.557053614
mouth_reversed 0.77672173 8.302717e-01 6.160912e-01 0.793254811 0.70466052 0.936112687
neck_forward 0.07033578 2.579308e-01 5.364661e-01 0.084589444 0.08632257 0.091175442
neck_reversed 0.17259799 7.165620e-02 7.069493e-01 0.304204139 0.20474801 0.252624987
facechest_forward 0.18102107 1.615537e-01 9.358722e-01 0.112046749 0.19514544 0.169752614
facechest_reversed 0.34868489 3.975328e-02 4.671999e-01 0.457300336 0.49164189 0.382738792
eyes_forward eyes_reversed mouth_forward mouth_reversed neck_forward neck_reversed
aoasl 1.481267e-01 2.813177e-01 4.162838e-01 7.767217e-01 7.033578e-02 1.725980e-01
signyrs 5.467283e-01 1.542104e-01 7.644726e-01 8.302717e-01 2.579308e-01 7.165620e-02
selfrate 9.881757e-01 3.788530e-01 8.693499e-01 6.160912e-01 5.364661e-01 7.069493e-01
gist_reversed 3.559903e-01 9.859236e-01 9.204923e-01 7.932548e-01 8.458944e-02 3.042041e-01
lex_forward 3.446869e-01 8.433655e-01 8.394476e-01 7.046605e-01 8.632257e-02 2.047480e-01
lex_reversed 6.708451e-01 5.588320e-01 5.570536e-01 9.361127e-01 9.117544e-02 2.526250e-01
eyes_forward NA 5.929025e-06 2.859607e-05 1.640479e-02 6.807338e-03 4.707219e-03
eyes_reversed 5.929025e-06 NA 8.840848e-03 2.065194e-03 1.019589e-02 4.996113e-04
mouth_forward 2.859607e-05 8.840848e-03 NA 6.461917e-05 7.135195e-01 7.692911e-01
mouth_reversed 1.640479e-02 2.065194e-03 6.461917e-05 NA 7.308466e-01 6.809521e-01
neck_forward 6.807338e-03 1.019589e-02 7.135195e-01 7.308466e-01 NA 2.398635e-05
neck_reversed 4.707219e-03 4.996113e-04 7.692911e-01 6.809521e-01 2.398635e-05 NA
facechest_forward 8.849713e-03 1.224782e-02 5.100229e-01 3.706863e-01 1.861311e-11 9.878377e-06
facechest_reversed 1.294574e-02 2.339301e-03 8.068219e-01 2.756272e-01 8.797435e-05 6.290368e-11
facechest_forward facechest_reversed
aoasl 1.810211e-01 3.486849e-01
signyrs 1.615537e-01 3.975328e-02
selfrate 9.358722e-01 4.671999e-01
gist_reversed 1.120467e-01 4.573003e-01
lex_forward 1.951454e-01 4.916419e-01
lex_reversed 1.697526e-01 3.827388e-01
eyes_forward 8.849713e-03 1.294574e-02
eyes_reversed 1.224782e-02 2.339301e-03
mouth_forward 5.100229e-01 8.068219e-01
mouth_reversed 3.706863e-01 2.756272e-01
neck_forward 1.861311e-11 8.797435e-05
neck_reversed 9.878377e-06 6.290368e-11
facechest_forward NA 7.177950e-07
facechest_reversed 7.177950e-07 NAcat(paste("","\n",""))corstarsl(eyeperf_hearing)All People Correlations, Forward
eyeperf_fwrv <- fulldata %>%
filter(eye_exclude == FALSE) %>%
select(participant, maingroup, hearing, direction, aoasl, signyrs, selfrate, acc, gist, eyes, mouth, neck, facechest) %>%
group_by(maingroup, participant, direction) %>%
mutate(gist = mean(gist, na.rm = TRUE),
lex = mean(acc, na.rm = TRUE),
eyes = mean(eyes, na.rm = TRUE),
mouth = mean(mouth, na.rm = TRUE),
neck = mean(neck, na.rm = TRUE),
facechest = mean(facechest, na.rm = TRUE)) %>%
ungroup() %>%
select(maingroup, participant, hearing, direction, aoasl, signyrs, selfrate, gist, lex, eyes, mouth, neck, facechest) %>%
distinct() %>%
gather(metric, value, gist:facechest) %>%
unite(metricvalue, c(metric, direction), sep = "_", remove = FALSE) %>%
select(-metric) %>%
spread(metricvalue, value) %>%
select(-participant, -maingroup, -hearing) %>%
select(direction, aoasl, signyrs, selfrate, gist_reversed, lex_forward, lex_reversed, eyes_forward, eyes_reversed,
mouth_forward, mouth_reversed, neck_forward, neck_reversed, facechest_forward, facechest_reversed)Grouping rowwise data frame strips rowwise natureeyeperf_fw <- eyeperf_fwrv %>% filter(direction == "forward") %>%
select(-direction, -gist_reversed, -lex_reversed, -eyes_reversed, -mouth_reversed, -neck_reversed, -facechest_reversed)
eyeperf_rv <- eyeperf_fwrv %>% filter(direction == "reversed") %>% select(-direction, -lex_forward, -eyes_forward, -mouth_forward, -neck_forward, -facechest_forward)
# Correlations for FW
print("FW Correlations - Pearson's r")[1] "FW Correlations - Pearson's r"#corstarsl(lexgist_deaf)
Hmisc::rcorr(as.matrix(eyeperf_fw))$r aoasl signyrs selfrate lex_forward eyes_forward mouth_forward
aoasl 1.00000000 -0.78522450 -0.5061627626 -0.14263168 0.1023350 -0.0600452
signyrs -0.78522450 1.00000000 0.7095934153 0.36334717 -0.0971436 0.2172435
selfrate -0.50616276 0.70959342 1.0000000000 0.46112436 -0.1184349 0.1191126
lex_forward -0.14263168 0.36334717 0.4611243606 1.00000000 -0.1346806 0.1166878
eyes_forward 0.10233497 -0.09714360 -0.1184348539 -0.13468060 1.0000000 -0.6230710
mouth_forward -0.06004520 0.21724346 0.1191126406 0.11668780 -0.6230710 1.0000000
neck_forward -0.12643635 -0.03886278 0.0937957317 0.05166910 -0.4273281 -0.4048121
facechest_forward 0.06745102 0.12098147 -0.0001684658 -0.03165489 0.4281833 0.4368729
neck_forward facechest_forward
aoasl -0.12643635 0.0674510226
signyrs -0.03886278 0.1209814698
selfrate 0.09379573 -0.0001684658
lex_forward 0.05166910 -0.0316548944
eyes_forward -0.42732805 0.4281833172
mouth_forward -0.40481210 0.4368729293
neck_forward 1.00000000 -0.9653760791
facechest_forward -0.96537608 1.0000000000print("FW Correlations - P-values")[1] "FW Correlations - P-values"Hmisc::rcorr(as.matrix(eyeperf_fw))$P aoasl signyrs selfrate lex_forward eyes_forward mouth_forward
aoasl NA 5.523138e-12 1.290503e-04 0.3131194667 4.703512e-01 6.724059e-01
signyrs 5.523138e-12 NA 3.880305e-09 0.0081064919 4.932766e-01 1.218575e-01
selfrate 1.290503e-04 3.880305e-09 NA 0.0005807587 4.030234e-01 4.003178e-01
lex_forward 3.131195e-01 8.106492e-03 5.807587e-04 NA 3.411308e-01 4.100460e-01
eyes_forward 4.703512e-01 4.932766e-01 4.030234e-01 0.3411308211 NA 8.097570e-07
mouth_forward 6.724059e-01 1.218575e-01 4.003178e-01 0.4100460282 8.097570e-07 NA
neck_forward 3.717642e-01 7.844425e-01 5.083628e-01 0.7160245249 1.579676e-03 2.913237e-03
facechest_forward 6.347056e-01 3.929129e-01 9.990543e-01 0.8237117786 1.542092e-03 1.203117e-03
neck_forward facechest_forward
aoasl 0.371764160 0.634705621
signyrs 0.784442500 0.392912923
selfrate 0.508362772 0.999054274
lex_forward 0.716024525 0.823711779
eyes_forward 0.001579676 0.001542092
mouth_forward 0.002913237 0.001203117
neck_forward NA 0.000000000
facechest_forward 0.000000000 NAcat(paste("","\n",""))corstarsl(eyeperf_fw)All People Correlations, Reversed
# Correlations for RV
print("RV Correlations - Pearson's r")[1] "RV Correlations - Pearson's r"#corstarsl(lexgist_hearing)
Hmisc::rcorr(as.matrix(eyeperf_rv))$r aoasl signyrs selfrate gist_reversed lex_reversed eyes_reversed
aoasl 1.00000000 -0.78501874 -0.50422978 -0.3232638240 -0.25779733 0.08366051
signyrs -0.78501874 1.00000000 0.70727050 0.2341087610 0.24984565 -0.02729736
selfrate -0.50422978 0.70727050 1.00000000 0.3761505187 0.35782853 0.01126565
gist_reversed -0.32326382 0.23410876 0.37615052 1.0000000000 0.44831455 -0.03314746
lex_reversed -0.25779733 0.24984565 0.35782853 0.4483145475 1.00000000 -0.14027905
eyes_reversed 0.08366051 -0.02729736 0.01126565 -0.0331474617 -0.14027905 1.00000000
mouth_reversed -0.08601686 0.25058311 0.13689724 0.1034677997 0.14056188 -0.54427344
neck_reversed -0.09436191 -0.11454841 -0.05930558 -0.0002581948 0.07954131 -0.50321871
facechest_reversed -0.00671559 0.23324074 0.17394754 0.0425947122 -0.02840233 0.48223892
mouth_reversed neck_reversed facechest_reversed
aoasl -0.08601686 -0.0943619087 -0.00671559
signyrs 0.25058311 -0.1145484149 0.23324074
selfrate 0.13689724 -0.0593055785 0.17394754
gist_reversed 0.10346780 -0.0002581948 0.04259471
lex_reversed 0.14056188 0.0795413107 -0.02840233
eyes_reversed -0.54427344 -0.5032187104 0.48223892
mouth_reversed 1.00000000 -0.3897527158 0.45460728
neck_reversed -0.38975272 1.0000000000 -0.95769274
facechest_reversed 0.45460728 -0.9576927423 1.00000000print("RV Correlations - P-values")[1] "RV Correlations - P-values"Hmisc::rcorr(as.matrix(eyeperf_rv))$P aoasl signyrs selfrate gist_reversed lex_reversed eyes_reversed
aoasl NA 9.197088e-12 1.615663e-04 0.0206764715 0.0677889791 5.594433e-01
signyrs 9.197088e-12 NA 6.557459e-09 0.0982310781 0.0770305388 8.491958e-01
selfrate 1.615663e-04 6.557459e-09 NA 0.0065211917 0.0099363432 9.374616e-01
gist_reversed 2.067647e-02 9.823108e-02 6.521192e-03 NA 0.0009694904 8.173808e-01
lex_reversed 6.778898e-02 7.703054e-02 9.936343e-03 0.0009694904 NA 3.261871e-01
eyes_reversed 5.594433e-01 8.491958e-01 9.374616e-01 0.8173807987 0.3261870511 NA
mouth_reversed 5.483940e-01 7.613369e-02 3.381006e-01 0.4699673532 0.3252028385 3.651371e-05
neck_reversed 5.101240e-01 4.234798e-01 6.793202e-01 0.9985652716 0.5790064531 1.673548e-04
facechest_reversed 9.626962e-01 9.952016e-02 2.221732e-01 0.7666339865 0.8431666495 3.391156e-04
mouth_reversed neck_reversed facechest_reversed
aoasl 5.483940e-01 0.5101239971 0.9626961855
signyrs 7.613369e-02 0.4234797695 0.0995201557
selfrate 3.381006e-01 0.6793201518 0.2221731526
gist_reversed 4.699674e-01 0.9985652716 0.7666339865
lex_reversed 3.252028e-01 0.5790064531 0.8431666495
eyes_reversed 3.651371e-05 0.0001673548 0.0003391156
mouth_reversed NA 0.0046967186 0.0008043284
neck_reversed 4.696719e-03 NA 0.0000000000
facechest_reversed 8.043284e-04 0.0000000000 NAcat(paste("","\n",""))corstarsl(eyeperf_rv)All People Correlations, Both Directions
# Correlations for All
print("ALL Correlations - Pearson's r")[1] "ALL Correlations - Pearson's r"#corstarsl(lexgist_all)
Hmisc::rcorr(as.matrix(eyeperf_all))$r aoasl signyrs selfrate gist_reversed lex_forward lex_reversed
aoasl 1.00000000 -0.78522450 -0.5061627626 -0.3232638240 -0.142631680 -0.25779733
signyrs -0.78522450 1.00000000 0.7095934153 0.2341087610 0.363347173 0.24984565
selfrate -0.50616276 0.70959342 1.0000000000 0.3761505187 0.461124361 0.35782853
gist_reversed -0.32326382 0.23410876 0.3761505187 1.0000000000 0.157367334 0.44831455
lex_forward -0.14263168 0.36334717 0.4611243606 0.1573673338 1.000000000 0.23866823
lex_reversed -0.25779733 0.24984565 0.3578285277 0.4483145475 0.238668233 1.00000000
eyes_forward 0.10233497 -0.09714360 -0.1184348539 -0.1583380550 -0.134680599 -0.13459931
eyes_reversed 0.08366051 -0.02729736 0.0112656485 -0.0331474617 -0.007873518 -0.14027905
mouth_forward -0.06004520 0.21724346 0.1191126406 0.1270669401 0.116687804 0.05451585
mouth_reversed -0.08601686 0.25058311 0.1368972361 0.1034677997 0.081888258 0.14056188
neck_forward -0.12643635 -0.03886278 0.0937957317 0.0342302844 0.051669102 0.10643122
neck_reversed -0.09436191 -0.11454841 -0.0593055785 -0.0002581948 0.003536053 0.07954131
facechest_forward 0.06745102 0.12098147 -0.0001684658 -0.0335624404 -0.031654894 -0.09553912
facechest_reversed -0.00671559 0.23324074 0.1739475429 0.0425947122 0.063717648 -0.02840233
eyes_forward eyes_reversed mouth_forward mouth_reversed neck_forward neck_reversed
aoasl 0.1023350 0.083660506 -0.06004520 -0.08601686 -0.12643635 -0.0943619087
signyrs -0.0971436 -0.027297357 0.21724346 0.25058311 -0.03886278 -0.1145484149
selfrate -0.1184349 0.011265649 0.11911264 0.13689724 0.09379573 -0.0593055785
gist_reversed -0.1583381 -0.033147462 0.12706694 0.10346780 0.03423028 -0.0002581948
lex_forward -0.1346806 -0.007873518 0.11668780 0.08188826 0.05166910 0.0035360528
lex_reversed -0.1345993 -0.140279055 0.05451585 0.14056188 0.10643122 0.0795413107
eyes_forward 1.0000000 0.706692934 -0.62307096 -0.38571414 -0.42732805 -0.3926679790
eyes_reversed 0.7066929 1.000000000 -0.36512721 -0.54427344 -0.38895777 -0.5032187104
mouth_forward -0.6230710 -0.365127206 1.00000000 0.70370090 -0.40481210 -0.2855360210
mouth_reversed -0.3857141 -0.544273436 0.70370090 1.00000000 -0.31946859 -0.3897527158
neck_forward -0.4273281 -0.388957769 -0.40481210 -0.31946859 1.00000000 0.7993095517
neck_reversed -0.3926680 -0.503218710 -0.28553602 -0.38975272 0.79930955 1.0000000000
facechest_forward 0.4281833 0.402936906 0.43687293 0.35403353 -0.96537608 -0.7889307141
facechest_reversed 0.3631589 0.482238919 0.34413567 0.45460728 -0.76219910 -0.9576927423
facechest_forward facechest_reversed
aoasl 0.0674510226 -0.00671559
signyrs 0.1209814698 0.23324074
selfrate -0.0001684658 0.17394754
gist_reversed -0.0335624404 0.04259471
lex_forward -0.0316548944 0.06371765
lex_reversed -0.0955391154 -0.02840233
eyes_forward 0.4281833172 0.36315894
eyes_reversed 0.4029369056 0.48223892
mouth_forward 0.4368729293 0.34413567
mouth_reversed 0.3540335298 0.45460728
neck_forward -0.9653760791 -0.76219910
neck_reversed -0.7889307141 -0.95769274
facechest_forward 1.0000000000 0.81137252
facechest_reversed 0.8113725185 1.00000000print("ALL Correlations - P-values")[1] "ALL Correlations - P-values"Hmisc::rcorr(as.matrix(eyeperf_all))$P aoasl signyrs selfrate gist_reversed lex_forward lex_reversed
aoasl NA 5.523138e-12 1.290503e-04 0.0206764715 0.3131194667 0.0677889791
signyrs 5.523138e-12 NA 3.880305e-09 0.0982310781 0.0081064919 0.0770305388
selfrate 1.290503e-04 3.880305e-09 NA 0.0065211917 0.0005807587 0.0099363432
gist_reversed 2.067647e-02 9.823108e-02 6.521192e-03 NA 0.2700899346 0.0009694904
lex_forward 3.131195e-01 8.106492e-03 5.807587e-04 0.2700899346 NA 0.0916709472
lex_reversed 6.778898e-02 7.703054e-02 9.936343e-03 0.0009694904 0.0916709472 NA
eyes_forward 4.703512e-01 4.932766e-01 4.030234e-01 0.2671087463 0.3411308211 0.3463483357
eyes_reversed 5.594433e-01 8.491958e-01 9.374616e-01 0.8173807987 0.9562699016 0.3261870511
mouth_forward 6.724059e-01 1.218575e-01 4.003178e-01 0.3742437833 0.4100460282 0.7039809873
mouth_reversed 5.483940e-01 7.613369e-02 3.381006e-01 0.4699673532 0.5678220006 0.3252028385
neck_forward 3.717642e-01 7.844425e-01 5.083628e-01 0.8115222617 0.7160245249 0.4572739900
neck_reversed 5.101240e-01 4.234798e-01 6.793202e-01 0.9985652716 0.9803528904 0.5790064531
facechest_forward 6.347056e-01 3.929129e-01 9.990543e-01 0.8151343981 0.8237117786 0.5048365664
facechest_reversed 9.626962e-01 9.952016e-02 2.221732e-01 0.7666339865 0.6568936305 0.8431666495
eyes_forward eyes_reversed mouth_forward mouth_reversed neck_forward neck_reversed
aoasl 4.703512e-01 5.594433e-01 6.724059e-01 5.483940e-01 3.717642e-01 5.101240e-01
signyrs 4.932766e-01 8.491958e-01 1.218575e-01 7.613369e-02 7.844425e-01 4.234798e-01
selfrate 4.030234e-01 9.374616e-01 4.003178e-01 3.381006e-01 5.083628e-01 6.793202e-01
gist_reversed 2.671087e-01 8.173808e-01 3.742438e-01 4.699674e-01 8.115223e-01 9.985653e-01
lex_forward 3.411308e-01 9.562699e-01 4.100460e-01 5.678220e-01 7.160245e-01 9.803529e-01
lex_reversed 3.463483e-01 3.261871e-01 7.039810e-01 3.252028e-01 4.572740e-01 5.790065e-01
eyes_forward NA 6.830266e-09 8.097570e-07 5.184763e-03 1.579676e-03 4.369926e-03
eyes_reversed 6.830266e-09 NA 8.425391e-03 3.651371e-05 4.789468e-03 1.673548e-04
mouth_forward 8.097570e-07 8.425391e-03 NA 8.423032e-09 2.913237e-03 4.224662e-02
mouth_reversed 5.184763e-03 3.651371e-05 8.423032e-09 NA 2.230426e-02 4.696719e-03
neck_forward 1.579676e-03 4.789468e-03 2.913237e-03 2.230426e-02 NA 2.037925e-12
neck_reversed 4.369926e-03 1.673548e-04 4.224662e-02 4.696719e-03 2.037925e-12 NA
facechest_forward 1.542092e-03 3.372262e-03 1.203117e-03 1.081053e-02 0.000000e+00 6.159073e-12
facechest_reversed 8.811831e-03 3.391156e-04 1.340835e-02 8.043284e-04 8.169354e-11 0.000000e+00
facechest_forward facechest_reversed
aoasl 6.347056e-01 9.626962e-01
signyrs 3.929129e-01 9.952016e-02
selfrate 9.990543e-01 2.221732e-01
gist_reversed 8.151344e-01 7.666340e-01
lex_forward 8.237118e-01 6.568936e-01
lex_reversed 5.048366e-01 8.431666e-01
eyes_forward 1.542092e-03 8.811831e-03
eyes_reversed 3.372262e-03 3.391156e-04
mouth_forward 1.203117e-03 1.340835e-02
mouth_reversed 1.081053e-02 8.043284e-04
neck_forward 0.000000e+00 8.169354e-11
neck_reversed 6.159073e-12 0.000000e+00
facechest_forward NA 5.182521e-13
facechest_reversed 5.182521e-13 NAcorstarsl(eyeperf_all)Correlation Table
And the correlation table.
ggpairs(eyeperf, columns = c(2:13), aes(color = hearing))
plot: [1,1] [=---------------------------------------------------------------------------] 1% est: 0s
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plot: [9,2] [====================================================------------------------] 68% est: 6s
plot: [9,3] [====================================================------------------------] 69% est: 6s
plot: [9,4] [=====================================================-----------------------] 69% est: 6s
plot: [9,5] [=====================================================-----------------------] 70% est: 6s
plot: [9,6] [======================================================----------------------] 71% est: 6s
plot: [9,7] [======================================================----------------------] 72% est: 5s
plot: [9,8] [=======================================================---------------------] 72% est: 5s
plot: [9,9] [=======================================================---------------------] 73% est: 5s
plot: [9,10] [=======================================================--------------------] 74% est: 5s
plot: [9,11] [========================================================-------------------] 74% est: 5s
plot: [9,12] [========================================================-------------------] 75% est: 5s
plot: [10,1] [=========================================================------------------] 76% est: 5s
plot: [10,2] [=========================================================------------------] 76% est: 5s
plot: [10,3] [==========================================================-----------------] 77% est: 4s
plot: [10,4] [==========================================================-----------------] 78% est: 4s
plot: [10,5] [===========================================================----------------] 78% est: 4s
plot: [10,6] [===========================================================----------------] 79% est: 4s
plot: [10,7] [============================================================---------------] 80% est: 4s
plot: [10,8] [============================================================---------------] 81% est: 4s
plot: [10,9] [=============================================================--------------] 81% est: 4s
plot: [10,10] [=============================================================-------------] 82% est: 4s
plot: [10,11] [=============================================================-------------] 83% est: 3s
plot: [10,12] [==============================================================------------] 83% est: 3s
plot: [11,1] [===============================================================------------] 84% est: 3s
plot: [11,2] [================================================================-----------] 85% est: 3s
plot: [11,3] [================================================================-----------] 85% est: 3s
plot: [11,4] [=================================================================----------] 86% est: 3s
plot: [11,5] [=================================================================----------] 87% est: 3s
plot: [11,6] [==================================================================---------] 88% est: 2s
plot: [11,7] [==================================================================---------] 88% est: 2s
plot: [11,8] [===================================================================--------] 89% est: 2s
plot: [11,9] [===================================================================--------] 90% est: 2s
plot: [11,10] [===================================================================-------] 90% est: 2s
plot: [11,11] [===================================================================-------] 91% est: 2s
plot: [11,12] [====================================================================------] 92% est: 2s
plot: [12,1] [=====================================================================------] 92% est: 1s
plot: [12,2] [======================================================================-----] 93% est: 1s
plot: [12,3] [======================================================================-----] 94% est: 1s
plot: [12,4] [=======================================================================----] 94% est: 1s
plot: [12,5] [=======================================================================----] 95% est: 1s
plot: [12,6] [========================================================================---] 96% est: 1s
plot: [12,7] [========================================================================---] 97% est: 1s
plot: [12,8] [=========================================================================--] 97% est: 1s
plot: [12,9] [=========================================================================--] 98% est: 0s
plot: [12,10] [=========================================================================-] 99% est: 0s
plot: [12,11] [=========================================================================-] 99% est: 0s
plot: [12,12] [==========================================================================]100% est: 0s
Heat Maps
And finally, we’re going to do heat maps.
eyegaze_heat <- fulldata %>%
ungroup() %>%
filter(eye_exclude == FALSE) %>%
select(id:direction, belly, lowerchest, midchest, upperchest, neck, mouth, eyes, forehead) %>%
gather(aoi, percent, belly:forehead) %>%
group_by(maingroup, participant, direction, aoi) %>%
summarise(percent = mean(percent, na.rm=TRUE)) %>%
group_by(maingroup,direction,aoi) %>%
summarise(percent = mean(percent, na.rm=TRUE)) %>%
ungroup() %>%
filter(!is.na(aoi)) %>%
mutate(aoi = factor(aoi,levels=c("belly","lowerchest","midchest",
"upperchest","neck","mouth","eyes","forehead"))) %>%
ungroup() %>%
mutate(maingroup = case_when(
maingroup == "DeafEarly" ~ "Deaf Early",
maingroup == "DeafLate" ~ "Deaf Late",
maingroup == "HearingLate" ~ "Hearing Late",
maingroup == "HearingNovice" ~ "Hearing Novice"
))
eyegaze_heat_all <- fulldata %>%
ungroup() %>%
filter(eye_exclude == FALSE) %>%
select(id:direction, belly, lowerchest, midchest, upperchest, neck, mouth, eyes, forehead) %>%
gather(aoi, percent, belly:forehead) %>%
group_by(maingroup,participant,direction,aoi) %>%
dplyr::summarize(percent = mean(percent, na.rm=TRUE)) %>%
group_by(maingroup,direction,aoi) %>%
dplyr::summarize(percent = mean(percent, na.rm=TRUE)) %>%
group_by(maingroup,aoi) %>%
dplyr::summarize(percent = mean(percent, na.rm=TRUE)) %>%
ungroup() %>%
filter(!is.na(aoi)) %>%
mutate(aoi = factor(aoi,levels=c("belly","lowerchest","midchest",
"upperchest","neck","mouth","eyes","forehead"))) %>%
ungroup() %>%
mutate(maingroup = case_when(
maingroup == "DeafEarly" ~ "Deaf Early",
maingroup == "DeafLate" ~ "Deaf Late",
maingroup == "HearingLate" ~ "Hearing Late",
maingroup == "HearingNovice" ~ "Hearing Novice"
))
ggplot(eyegaze_heat, aes(x = maingroup, y = aoi)) +
geom_tile(aes(fill=percent),color="lightgray",na.rm=TRUE) +
scale_fill_viridis(option = "viridis", direction=-1, limits = c(0,.71), labels = percent, name = "looking time") +
theme_bw() +
theme(axis.text.x=element_text(angle=30,hjust=1),
strip.text.x = element_text(size = 11, color = "black", face = "italic"),
strip.background = element_rect(colour = "white", fill = "white"),
panel.grid.major = element_line(color = "white")) +
facet_grid(. ~ direction) +
ylab("") + xlab("") + ggtitle("Eye Gaze Heat Map, by Direction") +
scale_y_discrete(expand=c(0,0)) +
scale_x_discrete(expand = c(0,0))
ggplot(eyegaze_heat_all, aes(x = maingroup, y = aoi)) +
geom_tile(aes(fill=percent),color="lightgray",na.rm=TRUE) +
scale_fill_viridis(option = "viridis", direction=-1, limits = c(0,.71), labels = percent, name = "looking time") +
theme_bw() +
theme(axis.text.x=element_text(angle=30,hjust=1),
panel.grid.major = element_line(color = "white")) +
ylab("") + xlab("") + ggtitle("Eye Gaze Heat Map (Direction Collapsed)") +
scale_y_discrete(expand=c(0,0)) +
scale_x_discrete(expand = c(0,0))
Summary
Below are the p-values from the ANOVAs with 4 MainGroups. I never included Age as a covariate because it never improved the model. I included all ANOVAs for Gist and Lex Recall, and ANOVAs for any eye AOI or ratio was included only if either maingroup or direction was significant. Deafearly-Deaflate shows the LSD p-value for that comparison.
results1 <- structure(list(model = c("gist-maingroup-both", "gist-maingroup-fw",
"gist-maingroup-rv", "lexrecall-maingroup-both", "lexrecall-maingroup-fw",
"lexrecall-maingroup-rv", "mouth-maingroup-both", "upperchest-maingroup-both",
"upperchest-maingroup-rv", "facechest-maingroup-both", "moutheye-maingroup-both"
), maingroup = c(0, 0, 0.01, 0, 0.04, 0.02, 0.06, 0, 0.01, 0.1,
0.05), direction = c(0, NA, NA, 0, NA, NA, 0.06, 0.16, NA, 0.07,
0.48), `deafearly-deaflate` = c(0.1, 0.69, 0.02, 0.11, 0.95,
0.06, 0.38, 0.94, 0.52, 0.08, 0.68)), .Names = c("model", "maingroup",
"direction", "deafearly-deaflate"), class = c("tbl_df", "tbl",
"data.frame"), row.names = c(NA, -11L))
results1And below are the p-values from the ANCOVAs with Hearing & AoASL. I included all ANCOVAs for Gist and Lex Recall, and ANCOVAs for any eye AOI or ratio was included only if any main factor was significant. LSD comparisons are not needed because there’s only 2 levels in each group!
results2 <- structure(list(model = c("gist-both", "gist-fw", "gist-rv", "lex-both",
"lex-fw", "lex-rv", "forehead-fw", "mouth-both", "mouth-rv",
"upperchest-both", "upperchest-rv", "facechest-both", "moutheye-both"
), hearing = c(0, 0.00, 0.01, 0.01, 0.22, 0.03, 0.06, 0.01,
0.04, 0.01, 0.01, 0.35, 0.07), direction = c(0, NA, NA, 0, NA,
NA, NA, 0.05, NA, 0.21, NA, 0.05, 0.52), aoasl = c(0.22, 0.77,
0.19, 0.56, 0.58, 0.25, 0.08, 0.06, 0.12, 0.68, 0.95, 0.12, 0.44
), age = c(0.08, 0.01, 0.86, 0.09, 0.02, 0.7, 0.68, 0.28, 0.5,
0.02, 0.08, 0.00, 0.21)), .Names = c("model", "hearing", "direction",
"aoasl", "age"), class = c("tbl_df", "tbl", "data.frame"), row.names = c(NA,
-13L))
results2Finally, the correlations for Deaf and Hearing separately are not significant. But there are significant correlations across all participants. I worry it is caused by HearingNovice, though…
results3 <- tribble(
~ metric, ~ AoASLcorrelationRvalue, ~ Pvalue,
"gist-fw", -0.32, 0.019,
"gist-rv", -0.39, 0.004,
"lex-fw", -0.08, 0.567,
"lex-rv", -0.34, 0.014
)
results3Ternary Plots
Let’s make triangle plots. “What?” you say. Read on.
library(ggtern)
fulldata %>%
ggtern(aes(x = eyes, y = mouth, z = neck)) + facet_grid(direction ~ maingroup) + stat_density_tern(geom='polygon', aes(fill=..level..), bins=4) + geom_point(color = "white", alpha = 0.5) + theme_bw()
fulldata %>%
ggtern(aes(x = eyes, y = mouth, z = neck)) + facet_grid(direction ~ maingroup) + geom_confidence_tern(breaks = c(.5), color = "red") + geom_point() + theme_bw()
Rain’s Notes
About Adults:
-I think I want to write it up as an ANCOVA, with direction included. And LSD comparisons instead of Tukey. (I will do my own corrections) -You often have one liners summarizing results, in all tabs, those are nice, keep them coming. -(If you have reasons to present anything other than the ANCOVA, put that in your results tab)
I think if we do it this way then we get a really important story to tell: That the critical AoA cutoff is below 4 vs above 4 years of age (two groups 0-4 vs 4-13). This suggest early ASL is important.
Viewing Space Correlations
Okay now let’s work with the raw viewing space that we created in 05viewingspace. For each participant the first 30 samples was removed.
- box width
- upper limit
- lower limit
- left side
- right side
- height
- box center position
- area
correlated with
- gist
- lexical recall
- sign years
- aoasl
Here’s sample of the raw viewing data structure.
viewing <- read_csv('adultviewingspaceraw.csv', progress=FALSE)
head(viewing, 10)Now I’m going to compute the following metrics with the following definitions!
- upper (75th percentile on y-axis)
- lower (25th percentile on y-axis)
- left (25th percentile on x-axis)
- right (75th percentile on x-axis)
- width (x-axis IQR)
- height (y-axis IQR)
- x-center (median x-axis)
- y-center (median y-axis)
- area (x-axis IQR * y-axis IQR)
# compute viewing metrics
viewing_metrics <- viewing %>%
group_by(participant, direction, media) %>%
summarise(
upper = quantile(y,na.rm=TRUE)[[2]],
lower = quantile(y,na.rm=TRUE)[[4]],
left = quantile(x,na.rm=TRUE)[[2]],
right = quantile(x,na.rm=TRUE)[[4]],
width = IQR(x,na.rm=TRUE),
height = IQR(y,na.rm=TRUE),
x_center = median(x,na.rm=TRUE),
y_center = median(y,na.rm=TRUE),
area = width*height
)
# average across two stories for each direction
viewing_metrics <- viewing_metrics %>%
group_by(participant, direction) %>%
summarise_if(is.numeric, funs(mean(., na.rm = TRUE)))
head(viewing_metrics,10)The participant names are different than what we’ve been using, so I’m going to fix that. Then I’ll join the behavioral data with the viewing space metrics.
Notes to self - which Alicia (Deaf or 2?) - add Gina in - add Jesse in - add JJ in - Laura 1 or Laura 2 - MarlaMarks or MarlaH - add Matthew - add Ranem
Below is an example of the forward table for viewing space and behavioral data
# Fix participant names
partnames <- read_csv("partnames.csv") %>%
distinct() %>%
mutate(participant = as.character(participant),
new_participant = as.character(new_participant))
viewing_metrics_fixed <- viewing_metrics %>%
ungroup() %>%
mutate(participant = as.character(participant)) %>%
left_join(partnames, by = "participant") %>%
select(-participant) %>%
rename(participant = new_participant) %>%
filter(!is.na(participant)) %>%
select(participant, direction:area)
# Get behavioral measures
eyeperf_fwrv <- fulldata %>%
filter(eye_exclude == FALSE) %>%
select(participant, maingroup, hearing, direction, aoasl, signyrs, acc, gist, eyes, mouth, neck, facechest) %>%
group_by(maingroup, participant, direction) %>%
mutate(gist = mean(gist, na.rm = TRUE),
lex = mean(acc, na.rm = TRUE),
eyes = mean(eyes, na.rm = TRUE),
mouth = mean(mouth, na.rm = TRUE),
neck = mean(neck, na.rm = TRUE),
facechest = mean(facechest, na.rm = TRUE)) %>%
ungroup() %>%
select(maingroup, participant, hearing, direction, aoasl, signyrs, gist, lex) %>%
distinct()
viewing_metrics_behav <-
left_join(viewing_metrics_fixed, eyeperf_fwrv, by = c("participant","direction")) %>%
filter(!is.na(maingroup)) %>%
select(participant, direction, maingroup:lex, upper:area) %>%
gather(metric, value, gist:area) %>%
unite(metricvalue, c(metric, direction), sep = "_", remove = FALSE) %>%
select(-metric) %>%
spread(metricvalue, value) %>%
select(-participant, -maingroup, -hearing)
viewing_fw <- viewing_metrics_behav %>%
filter(direction == "forward") %>%
select(aoasl,
signyrs,
gist_forward,
lex_forward,
upper_forward,
lower_forward,
left_forward,
right_forward,
width_forward,
height_forward,
x_center_forward,
y_center_forward,
area_forward)
viewing_rv <- viewing_metrics_behav %>%
filter(direction == "reversed") %>%
select(aoasl,
signyrs,
gist_reversed,
lex_reversed,
upper_reversed,
lower_reversed,
left_reversed,
right_reversed,
width_reversed,
height_reversed,
x_center_reversed,
y_center_reversed,
area_reversed)
head(viewing_fw,10)I’ll show the pretty correlations table first. Forward and reversed in that order.
corstarsl(viewing_fw)corstarsl(viewing_rv)Now the Pearson’s r correlations and p-values for forward and reversed stories in that order.
# Correlations for FW
print("FW Correlations - Pearson's r")[1] "FW Correlations - Pearson's r"Hmisc::rcorr(as.matrix(viewing_fw))$r aoasl signyrs gist_forward lex_forward upper_forward lower_forward
aoasl 1.00000000 -0.798933089 -0.336114883 -0.12550496 -0.052675061 -0.03179020
signyrs -0.79893309 1.000000000 0.494851708 0.31887597 0.113070615 0.06900509
gist_forward -0.33611488 0.494851708 1.000000000 0.48497108 -0.002796938 -0.06412645
lex_forward -0.12550496 0.318875968 0.484971076 1.00000000 0.079420246 0.02409866
upper_forward -0.05267506 0.113070615 -0.002796938 0.07942025 1.000000000 0.90333015
lower_forward -0.03179020 0.069005087 -0.064126454 0.02409866 0.903330147 1.00000000
left_forward -0.07841864 0.023459245 0.288385659 0.36911890 0.035317089 -0.16047448
right_forward -0.01384794 -0.006170828 0.252090007 0.30502945 0.024325686 0.03731066
width_forward 0.08813576 -0.042470839 -0.009999836 -0.04033331 -0.011447032 0.28287110
height_forward 0.04056910 -0.085305087 -0.143019065 -0.11658001 -0.073088787 0.36177546
x_center_forward -0.04673620 0.022889173 0.250142246 0.31041324 0.100249827 0.04587874
y_center_forward -0.06283791 0.124698378 -0.011492316 0.07408915 0.973598182 0.96471453
area_forward 0.04390561 0.002956281 0.004068306 -0.03552552 0.069223516 0.38586050
left_forward right_forward width_forward height_forward x_center_forward
aoasl -0.07841864 -0.013847941 0.088135764 0.04056910 -0.04673620
signyrs 0.02345924 -0.006170828 -0.042470839 -0.08530509 0.02288917
gist_forward 0.28838566 0.252090007 -0.009999836 -0.14301907 0.25014225
lex_forward 0.36911890 0.305029452 -0.040333308 -0.11658001 0.31041324
upper_forward 0.03531709 0.024325686 -0.011447032 -0.07308879 0.10024983
lower_forward -0.16047448 0.037310660 0.282871097 0.36177546 0.04587874
left_forward 1.00000000 0.777222157 -0.186017036 -0.44987059 0.87137234
right_forward 0.77722216 1.000000000 0.473667562 0.03388043 0.96054983
width_forward -0.18601704 0.473667562 1.000000000 0.68257123 0.28028715
height_forward -0.44987059 0.033880435 0.682571232 1.00000000 -0.11121073
x_center_forward 0.87137234 0.960549831 0.280287147 -0.11121073 1.00000000
y_center_forward -0.03671722 0.079427689 0.175419226 0.12701502 0.12332176
area_forward -0.22488306 0.410727978 0.956118166 0.74669957 0.25475150
y_center_forward area_forward
aoasl -0.06283791 0.043905608
signyrs 0.12469838 0.002956281
gist_forward -0.01149232 0.004068306
lex_forward 0.07408915 -0.035525519
upper_forward 0.97359818 0.069223516
lower_forward 0.96471453 0.385860503
left_forward -0.03671722 -0.224883065
right_forward 0.07942769 0.410727978
width_forward 0.17541923 0.956118166
height_forward 0.12701502 0.746699572
x_center_forward 0.12332176 0.254751503
y_center_forward 1.00000000 0.266658902
area_forward 0.26665890 1.000000000print("FW Correlations - P-values")[1] "FW Correlations - P-values"Hmisc::rcorr(as.matrix(viewing_fw))$P aoasl signyrs gist_forward lex_forward upper_forward lower_forward
aoasl NA 1.691669e-11 0.0208941517 0.4005829572 0.7251067 0.832007573
signyrs 1.691669e-11 NA 0.0004062851 0.0289167875 0.4492109 0.644880173
gist_forward 2.089415e-02 4.062851e-04 NA 0.0005506435 0.9851135 0.668482293
lex_forward 4.005830e-01 2.891679e-02 0.0005506435 NA 0.5956574 0.872261493
upper_forward 7.251067e-01 4.492109e-01 0.9851135120 0.5956573620 NA 0.000000000
lower_forward 8.320076e-01 6.448802e-01 0.6684822929 0.8722614935 0.0000000 NA
left_forward 6.003185e-01 8.756238e-01 0.0493191914 0.0106713559 0.8136845 0.281241248
right_forward 9.263924e-01 9.671634e-01 0.0873705003 0.0370839867 0.8710682 0.803370181
width_forward 5.557900e-01 7.768297e-01 0.9468117763 0.7877950615 0.9391278 0.054029672
height_forward 7.865834e-01 5.685996e-01 0.3375440458 0.4351688401 0.6253850 0.012465092
x_center_forward 7.550786e-01 8.786233e-01 0.0899260542 0.0337078420 0.5025623 0.759436726
y_center_forward 6.747705e-01 4.036413e-01 0.9388874529 0.6206470898 0.0000000 0.000000000
area_forward 7.694933e-01 9.842655e-01 0.9783481062 0.8126046451 0.6438313 0.007390725
left_forward right_forward width_forward height_forward x_center_forward
aoasl 6.003185e-01 9.263924e-01 5.557900e-01 7.865834e-01 7.550786e-01
signyrs 8.756238e-01 9.671634e-01 7.768297e-01 5.685996e-01 8.786233e-01
gist_forward 4.931919e-02 8.737050e-02 9.468118e-01 3.375440e-01 8.992605e-02
lex_forward 1.067136e-02 3.708399e-02 7.877951e-01 4.351688e-01 3.370784e-02
upper_forward 8.136845e-01 8.710682e-01 9.391278e-01 6.253850e-01 5.025623e-01
lower_forward 2.812412e-01 8.033702e-01 5.402967e-02 1.246509e-02 7.594367e-01
left_forward NA 1.326836e-10 2.106103e-01 1.510770e-03 1.776357e-15
right_forward 1.326836e-10 NA 7.711456e-04 8.211370e-01 0.000000e+00
width_forward 2.106103e-01 7.711456e-04 NA 1.259448e-07 5.635833e-02
height_forward 1.510770e-03 8.211370e-01 1.259448e-07 NA 4.567523e-01
x_center_forward 1.776357e-15 0.000000e+00 5.635833e-02 4.567523e-01 NA
y_center_forward 8.064371e-01 5.956228e-01 2.382381e-01 3.948934e-01 4.088923e-01
area_forward 1.285747e-01 4.134393e-03 0.000000e+00 1.676622e-09 8.397045e-02
y_center_forward area_forward
aoasl 0.67477052 7.694933e-01
signyrs 0.40364133 9.842655e-01
gist_forward 0.93888745 9.783481e-01
lex_forward 0.62064709 8.126046e-01
upper_forward 0.00000000 6.438313e-01
lower_forward 0.00000000 7.390725e-03
left_forward 0.80643712 1.285747e-01
right_forward 0.59562278 4.134393e-03
width_forward 0.23823808 0.000000e+00
height_forward 0.39489340 1.676622e-09
x_center_forward 0.40889226 8.397045e-02
y_center_forward NA 7.000816e-02
area_forward 0.07000816 NAcat(paste("","\n",""))# Correlations for RV
print("RV Correlations - Pearson's r")[1] "RV Correlations - Pearson's r"Hmisc::rcorr(as.matrix(viewing_rv))$r aoasl signyrs gist_reversed lex_reversed upper_reversed lower_reversed
aoasl 1.00000000 -0.79883313 -0.381241322 -0.30424419 -0.06355477 0.02613542
signyrs -0.79883313 1.00000000 0.280629128 0.28596225 0.11440074 0.02543912
gist_reversed -0.38124132 0.28062913 1.000000000 0.43307346 -0.10245936 -0.17444347
lex_reversed -0.30424419 0.28596225 0.433073461 1.00000000 0.11046334 -0.02827794
upper_reversed -0.06355477 0.11440074 -0.102459356 0.11046334 1.00000000 0.87952572
lower_reversed 0.02613542 0.02543912 -0.174443468 -0.02827794 0.87952572 1.00000000
left_reversed -0.10582226 0.04956812 0.105713964 -0.01805082 -0.11335842 -0.20099784
right_reversed -0.10692181 0.08757170 0.006426105 -0.05163386 -0.23821971 -0.14933449
width_reversed -0.01207225 0.07757059 -0.180515379 -0.06612321 -0.25019914 0.08018453
height_reversed 0.18343017 -0.18446210 -0.139683038 -0.28434381 -0.29129812 0.19901058
x_center_reversed -0.11743777 0.08409818 0.058511861 -0.02471087 -0.17340776 -0.17807971
y_center_reversed -0.05099373 0.11000430 -0.127016783 0.07911893 0.98551267 0.93859512
area_reversed 0.10099926 -0.01414519 -0.200223237 -0.22006452 -0.26817927 0.17402732
left_reversed right_reversed width_reversed height_reversed x_center_reversed
aoasl -0.10582226 -0.106921814 -0.01207225 0.1834301651 -0.1174377650
signyrs 0.04956812 0.087571703 0.07757059 -0.1844621003 0.0840981752
gist_reversed 0.10571396 0.006426105 -0.18051538 -0.1396830380 0.0585118607
lex_reversed -0.01805082 -0.051633861 -0.06612321 -0.2843438089 -0.0247108676
upper_reversed -0.11335842 -0.238219708 -0.25019914 -0.2912981212 -0.1734077632
lower_reversed -0.20099784 -0.149334490 0.08018453 0.1990105808 -0.1780797094
left_reversed 1.00000000 0.858386040 -0.17751643 -0.1706206948 0.9663785100
right_reversed 0.85838604 1.000000000 0.35247907 0.1903882176 0.9510509372
width_reversed -0.17751643 0.352479070 1.00000000 0.6764751077 0.0615787692
height_reversed -0.17062069 0.190388218 0.67647511 1.0000000000 -0.0008776907
x_center_reversed 0.96637851 0.951050937 0.06157877 -0.0008776907 1.0000000000
y_center_reversed -0.13036732 -0.196038291 -0.13825317 -0.1427114308 -0.1598070413
area_reversed -0.23121382 0.241754636 0.88554716 0.9021623135 -0.0108726285
y_center_reversed area_reversed
aoasl -0.05099373 0.10099926
signyrs 0.11000430 -0.01414519
gist_reversed -0.12701678 -0.20022324
lex_reversed 0.07911893 -0.22006452
upper_reversed 0.98551267 -0.26817927
lower_reversed 0.93859512 0.17402732
left_reversed -0.13036732 -0.23121382
right_reversed -0.19603829 0.24175464
width_reversed -0.13825317 0.88554716
height_reversed -0.14271143 0.90216231
x_center_reversed -0.15980704 -0.01087263
y_center_reversed 1.00000000 -0.13003346
area_reversed -0.13003346 1.00000000print("RV Correlations - P-values")[1] "RV Correlations - P-values"Hmisc::rcorr(as.matrix(viewing_rv))$P aoasl signyrs gist_reversed lex_reversed upper_reversed lower_reversed
aoasl NA 2.871614e-11 0.008946517 0.039810336 6.747694e-01 8.631151e-01
signyrs 2.871614e-11 NA 0.058879077 0.054034664 4.490254e-01 8.667286e-01
gist_reversed 8.946517e-03 5.887908e-02 NA 0.002644938 4.980410e-01 2.462568e-01
lex_reversed 3.981034e-02 5.403466e-02 0.002644938 NA 4.648882e-01 8.520140e-01
upper_reversed 6.747694e-01 4.490254e-01 0.498040978 0.464888182 NA 8.881784e-16
lower_reversed 8.631151e-01 8.667286e-01 0.246256809 0.852013954 8.881784e-16 NA
left_reversed 4.839657e-01 7.435646e-01 0.484415695 0.905222299 4.531955e-01 1.804297e-01
right_reversed 4.794092e-01 5.627861e-01 0.966191858 0.733263412 1.108762e-01 3.219149e-01
width_reversed 9.365335e-01 6.083685e-01 0.229940004 0.662397337 9.353790e-02 5.963023e-01
height_reversed 2.223771e-01 2.197413e-01 0.354521777 0.055469588 4.951439e-02 1.848672e-01
x_center_reversed 4.369955e-01 5.784363e-01 0.699306689 0.870510811 2.491163e-01 2.363937e-01
y_center_reversed 7.364507e-01 4.667569e-01 0.400249211 0.601208497 0.000000e+00 0.000000e+00
area_reversed 5.042169e-01 9.256639e-01 0.182150229 0.141681572 7.154661e-02 2.474031e-01
left_reversed right_reversed width_reversed height_reversed x_center_reversed
aoasl 4.839657e-01 4.794092e-01 9.365335e-01 2.223771e-01 0.4369955
signyrs 7.435646e-01 5.627861e-01 6.083685e-01 2.197413e-01 0.5784363
gist_reversed 4.844157e-01 9.661919e-01 2.299400e-01 3.545218e-01 0.6993067
lex_reversed 9.052223e-01 7.332634e-01 6.623973e-01 5.546959e-02 0.8705108
upper_reversed 4.531955e-01 1.108762e-01 9.353790e-02 4.951439e-02 0.2491163
lower_reversed 1.804297e-01 3.219149e-01 5.963023e-01 1.848672e-01 0.2363937
left_reversed NA 2.442491e-14 2.379037e-01 2.569221e-01 0.0000000
right_reversed 2.442491e-14 NA 1.628315e-02 2.050234e-01 0.0000000
width_reversed 2.379037e-01 1.628315e-02 NA 2.463920e-07 0.6843460
height_reversed 2.569221e-01 2.050234e-01 2.463920e-07 NA 0.9953811
x_center_reversed 0.000000e+00 0.000000e+00 6.843460e-01 9.953811e-01 NA
y_center_reversed 3.878327e-01 1.916489e-01 3.595180e-01 3.440807e-01 0.2887481
area_reversed 1.220937e-01 1.055233e-01 4.440892e-16 0.000000e+00 0.9428292
y_center_reversed area_reversed
aoasl 0.7364507 5.042169e-01
signyrs 0.4667569 9.256639e-01
gist_reversed 0.4002492 1.821502e-01
lex_reversed 0.6012085 1.416816e-01
upper_reversed 0.0000000 7.154661e-02
lower_reversed 0.0000000 2.474031e-01
left_reversed 0.3878327 1.220937e-01
right_reversed 0.1916489 1.055233e-01
width_reversed 0.3595180 4.440892e-16
height_reversed 0.3440807 0.000000e+00
x_center_reversed 0.2887481 9.428292e-01
y_center_reversed NA 3.890596e-01
area_reversed 0.3890596 NAcat(paste("","\n",""))---
title: "The Last Data Analysis to Rule Them All? (study1adults)"
author: "Adam Stone, PhD"
date: '`r format(Sys.Date(), "%m-%d-%Y")`'
output:
  html_notebook:
    code_folding: hide
    highlight: tango
    theme: paper
    toc: yes
    toc_depth: 2
    toc_float: yes
---

```{r global_options, include=FALSE}
knitr::opts_chunk$set(warning=FALSE, message=FALSE)
```

# Putting It All Back Together

Throughout all the data analysis we've done, the datasets have become more fragmented - lexical recall, gist, and eye tracking datasets. I want to put them all together in one whole dataset again so we can perform some analyses more efficiently (particularly correlations). The only thing I need to remember is we'll have a new column called `eye_exclude` and if it is set to `TRUE` it means we can't include that row in any analysis relating to eye gaze (usually because that trial was less than 25% looking). 

```{r smash data together, message=FALSE, warning=FALSE}
# Libraries
library(tidyverse)
library(lme4)
library(lmerTest)
library(scales)
library(viridis)
library(agricolae) 
library(GGally)
library(ez)

# Load lex and eye data
cleanlexdata <- read_csv("cleandata.csv") %>%
  select(-(forehead:total))

cleaneyedata <- read_csv("cleanpercentdata.csv") %>%
  spread(aoi,percent) %>%
  add_column(eye_exclude = FALSE)

# What rows were removed from the eye data back in 03eyegaze? Let's add back in
# With a new column - eye_exclude
removed <- anti_join(cleanlexdata, cleaneyedata) %>%
  add_column(eye_exclude = TRUE)
eyelexdata <- bind_rows(cleaneyedata, removed)

# Load gist data
gist <- read_csv('gist_indiv.csv', col_types = cols(
  participant = col_character(),
  gist.fw1 = col_integer(),
  gist.rv2 = col_integer(),
  gist.fw3 = col_integer(),
  gist.rv4 = col_integer()
)) %>%
  gather(video, gist, gist.fw1:gist.rv4) %>%
  mutate(video = str_sub(video,6,8))

# Presto, our full reunified dataset - 'fulldata'
# But I want to remove columns I don't want anymore and will recalculate later
fulldata <- left_join(eyelexdata, gist) %>%
  select(-moutheye, -facechest, -face, -chest)
```

# Group Changes and Participant Tables

We have some changes to make to the groups. First, fix Josh as learning ASL when he was 6. Next, drop the DeafNative Group and reclassify all who learned ASL < 3.9 as DeafEarly and ASL => 4.0 as DeafLate. 

```{r groups and participant tables}
# Change Josh's AoASL to 6
fulldata <- fulldata %>%
  mutate(aoasl = as.double(aoasl)) %>%
  mutate(aoasl = case_when(
    participant == "Josh" ~ 6,
    TRUE ~ aoasl
  ))

# Reclassify Groups
fulldata <- fulldata %>%
  mutate(maingroup = case_when(
    hearing == "Deaf" & aoasl < 4 ~ "DeafEarly",
    hearing == "Deaf" & aoasl >= 4 ~ "DeafLate",
    maingroup == "HearingLateASL" ~ "HearingLate",
    maingroup == "HearingNoviceASL" ~ "HearingNovice"
  ))

# Create Participant Demographics Table
participant_info <- fulldata %>%
  select(-(acc:gist)) %>%
  select(-(video:direction)) %>%
  distinct() %>% 
  group_by(maingroup) %>%
  summarise(n = n(),
            age_mean = mean(age),
            age_sd = sd(age),
            aoasl_mean = mean(aoasl),
            aoasl_sd = sd(aoasl),
            signyrs_mean = mean(signyrs),
            signyrs_sd = sd(signyrs),
            selfrate_mean = mean(selfrate),
            selfrate_sd = sd(selfrate)) %>%
  ungroup() %>%
  mutate_if(is.double, funs(round(., 2))) %>%
  mutate(age = paste(age_mean, "±", age_sd, sep = " "),
         aoasl = paste(aoasl_mean, "±", aoasl_sd, sep = " "),
         signyrs = paste(signyrs_mean, "±", signyrs_sd, sep = " "),
         selfrate = paste(selfrate_mean, "±", selfrate_sd, sep = " ")) %>%
  select(-(age_mean:selfrate_sd))

participant_info
write_csv(fulldata, "finaldataset.csv")

data_lowaoi <- fulldata %>% select(participant, story, belly:upperchest) %>% select(-eyes, -mouth, -chin) %>% gather(aoi, percent, belly:upperchest)
data_lowaoi$percent[is.na(data_lowaoi$percent)] <- 0
mean(data_lowaoi$percent, na.rm=TRUE)
sd(data_lowaoi$percent, na.rm=TRUE)
```
## Participant ANOVAs 

Below are the ANOVA outputs for participant demographics, and LSDs for each. 

Participants' age
```{r subject anova age, echo=FALSE, warning=FALSE}
# Make the participant data frame
p_anovas <- fulldata %>%
  select(-(acc:gist)) %>%
  select(-(video:direction)) %>%
  distinct() %>%
  select(maingroup:aoasl)

# Age ANOVA
p_age <- aov(age ~ maingroup, data = p_anovas)
summary(p_age)
p_age_lsd <- LSD.test(p_age, "maingroup", group = FALSE)$comparison
p_age_lsd
```


Participants' AoASL
```{r subject anova aoasl, echo=FALSE, warning=FALSE}
# AoASL ANOVA
p_aoasl <- aov(aoasl ~ maingroup, data = p_anovas)
summary(p_aoasl)
p_aoasl_lsd <- LSD.test(p_aoasl, "maingroup", group = FALSE)$comparison
p_aoasl_lsd
```

Participants' Sign Yrs
```{r subject anova signyrs, echo=FALSE, warning=FALSE}
# Signyrs ANOVA
p_signyrs <- aov(signyrs ~ maingroup, data = p_anovas)
summary(p_signyrs)
p_signyrs_lsd <- LSD.test(p_signyrs, "maingroup", group = FALSE)$comparison
p_signyrs_lsd
```

Participants' Self-Rating
```{r subject anova selfrate, echo=FALSE, warning=FALSE}
# Selfrate ANOVA
p_selfrate <- aov(selfrate ~ maingroup, data = p_anovas)
summary(p_selfrate)
p_selfrate_lsd <- LSD.test(p_selfrate, "maingroup", group = FALSE)$comparison
p_selfrate_lsd
```

# Gist & Lexical Recall Data

## Tables & Charts

Let's generate a table for lexical recall and gist for forward vs. reversed stories. 

```{r lex & gist table}
lexgist_info <- fulldata %>%
  group_by(maingroup, direction) %>%
  summarise(lex_mean = mean(acc, na.rm = TRUE),
            lex_sd = sd(acc, na.rm = TRUE),
            gist_mean = mean(gist),
            gist_sd = sd(gist)) %>%
  ungroup() %>%
  mutate_if(is.double, funs(round(., 2))) %>% 
  mutate(lex = paste(lex_mean, "±", lex_sd, sep = " "),
         gist = paste(gist_mean, "±", gist_sd, sep = " ")) %>%
  select(-(lex_mean:gist_sd)) %>%
  gather(metric, value, lex:gist) %>%
  unite("metric", c(metric, direction), sep = "_") %>%
  spread(metric, value) %>%
  print()
```

And then bar charts too after that with error bars. 

```{r lex and gist bar charts}
# Gist bar chart
gist_bar <- fulldata %>% select(participant, maingroup, direction, gist) %>%
  group_by(maingroup, participant, direction) %>%
  summarise(gist = mean(gist)) %>%
  group_by(maingroup, direction) %>%
  summarise(mean = mean(gist),
            sd = sd(gist),
            count = n(),
            se = sd/sqrt(count)) %>%
  ungroup() %>%
  mutate(maingroup = case_when(
    maingroup == "DeafEarly" ~ "Deaf Early",
    maingroup == "DeafLate" ~ "Deaf Late",
    maingroup == "HearingLate" ~ "Hearing Late",
    maingroup == "HearingNovice" ~ "Hearing Novice"
    ))

ggplot(gist_bar, aes(x = maingroup, y = mean, fill = direction)) +
  geom_bar(stat = "identity", position = position_dodge()) + 
  geom_errorbar(aes(ymin = mean-se, ymax = mean+se), position = position_dodge(0.9), width = 0.5) +
  labs(title = "Gist", x = "", y = "mean gist accuracy") +
  scale_y_continuous(labels = percent, limits = c(0,1)) + theme_bw()

# Lex bar chart
lex_bar <- fulldata %>% select(participant, maingroup, direction, acc) %>%
  group_by(maingroup, participant, direction) %>%
  summarise(acc = mean(acc, na.rm = TRUE)) %>%
  group_by(maingroup, direction) %>%
  summarise(mean = mean(acc, na.rm = TRUE),
            sd = sd(acc, na.rm = TRUE),
            count = n(),
            se = sd/sqrt(count)) %>%
  ungroup() %>%
  mutate(maingroup = case_when(
    maingroup == "DeafEarly" ~ "Deaf Early",
    maingroup == "DeafLate" ~ "Deaf Late",
    maingroup == "HearingLate" ~ "Hearing Late",
    maingroup == "HearingNovice" ~ "Hearing Novice"
    ))

ggplot(lex_bar, aes(x = maingroup, y = mean, fill = direction)) +
  geom_bar(stat = "identity", position = position_dodge()) + 
  geom_errorbar(aes(ymin = mean-se, ymax = mean+se), position = position_dodge(0.9), width = 0.5) +
  labs(title = "Lexical Recall", x = "", y = "mean lexical recall accuracy") +
  scale_y_continuous(labels = percent, limits = c(0,1)) +
  geom_hline(yintercept = .5, linetype = "dotted") +
  coord_cartesian(ylim = c(.5,1)) + theme_bw()
```

And let's calculate the average reduction in score due to reversal first is lex recall, then gist.

```{r}
reversal_lex <- fulldata %>%
  group_by(id, direction) %>%
  summarise(lex_mean = mean(acc, na.rm = TRUE)) %>%
  spread(direction, lex_mean) %>%
  group_by(id) %>%
  mutate(reversal = forward - reversed) %>%
  ungroup()

paste("lex mean", mean(reversal_lex$reversal))
paste("lex sd", sd(reversal_lex$reversal))

reversal_gist <- fulldata %>%
  group_by(id, direction) %>%
  summarise(gist_mean = mean(gist, na.rm = TRUE)) %>%
  spread(direction, gist_mean) %>%
  group_by(id) %>%
  mutate(reversal = forward - reversed) %>%
  ungroup()

paste("gist mean", mean(reversal_gist$reversal))
paste("gist sd", sd(reversal_gist$reversal))


```


## ANOVA Plan
Next, we're going to do ANOVAs. We'll always do it in this order.

1. ANOVA with factors MainGroup & Direction
2. ANOVA with factor MainGroup, for Forward only
3. ANOVA with factor MainGroup, for Reverse only 

(I also ran ANCOVAs before but now have taken them out...they are below: (4) ANCOVA with factor Direction, and covariate AoASL and Age, (5) Regression with variables AoASL and Age, for Forward only, (6) Regression with variables AoASL and Age, for Reverse only.) 

I did not include Age as a covariate in the first 3 ANOVAs because they did not add to or change the model in any significant way. 

## Gist ANOVAs

1. ANOVA with factors MainGroup & Direction.

```{r gist anova1, echo=TRUE, results = 'markup'}
# First let's make the participant-level dataset with which we'll do our ANCOVAs. 
participant_data <- fulldata %>%
  group_by(maingroup, participant, direction) %>%
  mutate(gist = mean(gist, na.rm = TRUE),
         acc = mean(acc, na.rm = TRUE)) %>%
  ungroup() %>%
  select(id, participant, hearing, maingroup, direction, age, aoasl, acc, gist) %>%
  distinct() %>%
  mutate(id = factor(id),
         participant = factor(participant),
         hearing = factor(hearing),
         maingroup = factor(maingroup),
         direction = factor(direction))

# # Gist ANOVA 1
# gist_aov1 <- aov(gist ~ maingroup * direction, data = participant_data)
# summary(gist_aov1)
# gist_lsd1 <- LSD.test(gist_aov1, "maingroup", group = FALSE)
# gist_lsd1$comparison

# Gist EZ ANOVA
ezANOVA(
  data = participant_data,
  dv = gist,
  wid = id,
  within = direction,
  between = maingroup,
  type = 3
)["ANOVA"]
```

2. ANOVA with factor MainGroup, for Forward only. *In the code is a Kruskal-Wallis test. And Chi-Sq too.*

```{r gist anova2, echo=TRUE, results= 'markup'}
# # Gist ANOVA 2
# gist_aov2 <- aov(gist ~ maingroup, data = filter(participant_data, direction == "forward"))
# summary(gist_aov2)
# gist_lsd2 <- LSD.test(gist_aov2, "maingroup", group = FALSE)
# gist_lsd2$comparison

ezANOVA(
  data = filter(participant_data, direction == "forward"),
  dv = gist,
  wid = id,
  between = maingroup,
  type = 3
)["ANOVA"]

# # KW Non-parametric test (like one-way ANOVA)
# kruskal.test(gist ~ maingroup, data = as.matrix(filter(participant_data, direction == "forward")))
# 
# # Chi Sq
# gist_chisq_fw <- participant_data %>%
#   ungroup() %>%
#   filter(direction == "forward") %>%
#   select(maingroup, gist) %>%
#   group_by(maingroup, gist) %>%
#   summarise(count = n()) %>%
#   spread(gist, count) %>%
#   rename(none = "0",
#          one = "0.5",
#          both = "1")
# 
# gist_chisq_fw[is.na(gist_chisq_fw)] <- 0L
# gist_chisq_fw <- cbind(gist_chisq_fw[,"none"], gist_chisq_fw[,"one"], gist_chisq_fw[,"both"])
# chisq.test(gist_chisq_fw)
```

3. ANOVA with factor MainGroup, for Reverse only. *In the code is a Kruskal-Wallis test. And Chi-Sq too.*

```{r gist anova3, echo=TRUE, results = 'markup'}
# # Gist ANOVA 3
# gist_aov3 <- aov(gist ~ maingroup, data = filter(participant_data, direction == "reversed"))
# summary(gist_aov3)
# gist_lsd3 <- LSD.test(gist_aov3, "maingroup", group = FALSE)
# gist_lsd3$comparison 

ezANOVA(
  data = filter(participant_data, direction == "reversed"),
  dv = gist,
  wid = id,
  between = maingroup,
  type = 3
)["ANOVA"]

# # KW Non-parametric test (like one-way ANOVA)
# kruskal.test(gist ~ maingroup, data = as.matrix(filter(participant_data, direction == "reversed")))
# 
# # Chi Sq
# gist_chisq_rv <- participant_data %>%
#   ungroup() %>%
#   filter(direction == "reversed") %>%
#   select(maingroup, gist) %>%
#   group_by(maingroup, gist) %>%
#   summarise(count = n()) %>%
#   spread(gist, count) %>%
#   rename(none = "0",
#          one = "0.5",
#          both = "1")
# 
# gist_chisq_rv[is.na(gist_chisq_rv)] <- 0L
# gist_chisq_rv <- cbind(gist_chisq_rv[,"none"], gist_chisq_rv[,"one"], gist_chisq_rv[,"both"])
# chisq.test(gist_chisq_rv)
```

## Lexical Recall ANOVAs

1. ANOVA with factors MainGroup & Direction.

```{r acc anova1, echo=TRUE, results = 'markup'}
# # Lexical Recall ANOVA 1
# acc_aov1 <- aov(acc ~ maingroup * direction, data = participant_data)
# summary(acc_aov1)
# acc_lsd1 <- LSD.test(acc_aov1, "maingroup", group = FALSE)
# acc_lsd1$comparison

ezANOVA(
  data = participant_data,
  dv = acc,
  wid = id,
  within = direction,
  between = maingroup,
  type = 3
)["ANOVA"]
```

2. ANOVA with factor MainGroup, for Forward only.

```{r acc anova2, echo=TRUE, results = 'markup'}
# # Lexical Recall ANOVA 2
# acc_aov2 <- aov(acc ~ maingroup, data = filter(participant_data, direction == "forward"))
# summary(acc_aov2)
# acc_lsd2 <- LSD.test(acc_aov2, "maingroup", group = FALSE)
# acc_lsd2$comparison

ezANOVA(
  data = filter(participant_data, direction == "forward"),
  dv = acc,
  wid = id,
  between = maingroup,
  type = 3
)["ANOVA"]
```

3. ANOVA with factor MainGroup, for Reverse only. 

```{r acc anova3, echo=TRUE, results = 'markup'}
# # Lexical Recall ANOVA 3
# acc_aov3 <- aov(acc ~ maingroup, data = filter(participant_data, direction == "reversed"))
# summary(acc_aov3)
# acc_lsd3 <- LSD.test(acc_aov3, "maingroup", group = FALSE)
# acc_lsd3$comparison

ezANOVA(
  data = filter(participant_data, direction == "reversed"),
  dv = acc,
  wid = id,
  between = maingroup,
  type = 3
)["ANOVA"]
```

# AoA Correlations

Next, we want to look at correlations between AoA and Gist, and betwen AoA and Lexical Recall. Rain asked for forward and reversed separately (1) deaf only, (2) hearing only, and (3) both. Let's make it work. 

```{r repackage data for correlations, message=FALSE, warning=FALSE}
# Let's make participant-level data, and have forward/reversed in separate columns
lexgist_data <- fulldata %>%
  group_by(maingroup, participant, direction) %>%
  mutate(gist = mean(gist, na.rm = TRUE),
         lex = mean(acc, na.rm = TRUE)) %>%
  ungroup() %>%
  select(maingroup, participant, hearing, direction, aoasl, signyrs, age, gist, lex) %>%
  distinct() %>%
  gather(metric, value, gist:lex) %>%
  unite(metricvalue, c(metric, direction), sep = "_") %>%
  spread(metricvalue, value) %>%
  select(-participant, -maingroup)

lexgist_deaf <- lexgist_data %>% filter(hearing == "Deaf") %>% select(-hearing)
lexgist_hearing <- lexgist_data %>% filter(hearing == "Hearing") %>% select(-hearing)
lexgist_all <- lexgist_data %>% select(-hearing)

# Load awesome function to make correlation tables with stars for significance
# From: https://myowelt.blogspot.co.uk/2008/04/beautiful-correlation-tables-in-r.html
corstarsl <- function(x){ 
require(Hmisc) 
x <- as.matrix(x) 
R <- Hmisc::rcorr(x)$r 
p <- Hmisc::rcorr(x)$P 
## define notions for significance levels; spacing is important.
mystars <- ifelse(p < .001, "***", ifelse(p < .01, "** ", ifelse(p < .05, "* ", " ")))
## trunctuate the matrix that holds the correlations to two decimal
R <- format(round(cbind(rep(-1.11, ncol(x)), R), 2))[,-1] 
## build a new matrix that includes the correlations with their apropriate stars 
Rnew <- matrix(paste(R, mystars, sep=""), ncol=ncol(x)) 
diag(Rnew) <- paste(diag(R), " ", sep="") 
rownames(Rnew) <- colnames(x) 
colnames(Rnew) <- paste(colnames(x), "", sep="") 
## remove upper triangle
Rnew <- as.matrix(Rnew)
Rnew[upper.tri(Rnew, diag = TRUE)] <- ""
Rnew <- as.data.frame(Rnew) 
## remove last column and return the matrix (which is now a data frame)
Rnew <- cbind(Rnew[1:length(Rnew)-1])
return(Rnew) 
}

# Correlations for Deaf
print("DEAF Correlations - Pearson's r")
#corstarsl(lexgist_deaf)
Hmisc::rcorr(as.matrix(lexgist_deaf))$r
print("DEAF Correlations - P-values")
Hmisc::rcorr(as.matrix(lexgist_deaf))$P
cat(paste("","\n",""))

# Correlations for Hearing
print("HEARING Correlations - Pearson's r")
#corstarsl(lexgist_hearing)
Hmisc::rcorr(as.matrix(lexgist_hearing))$r
print("HEARING Correlations - P-values")
Hmisc::rcorr(as.matrix(lexgist_hearing))$P
cat(paste("","\n",""))

# Correlations for All
print("ALL Correlations - Pearson's r")
#corstarsl(lexgist_all)
Hmisc::rcorr(as.matrix(lexgist_all))$r
print("ALL Correlations - P-values")
Hmisc::rcorr(as.matrix(lexgist_all))$P

```

I'm also including nicely formatted tables with *** indicators of significance for quick referencing. Order: Deaf, Hearing, All. 

```{r pretty correlations}
corstarsl(lexgist_deaf)
corstarsl(lexgist_hearing)
corstarsl(lexgist_all)
```

## Scatterplot of Correlations
Let's visualize what's happening with the correlations here. 
```{r correlation charts, fig.height=12, fig.width=12, message=FALSE, warning=FALSE}
ggpairs(lexgist_data, columns = c(2:8), aes(color = hearing))
```


# Eye Gaze Data

Now eye gaze data. Boxplots first. Also here, we're renaming "chin" to "neck" because that's what it actually is! But we also have to fix all NA's in the percentages to zeros, becuase that's what they actually are. 
```{r eye gaze boxplot}
# rename chin to neck
fulldata <- fulldata %>%
  rename(neck = chin) %>%
  gather(aoi, percent, belly:upperchest)

# Fix all NA's in Percent column to 0
fixpercent <- fulldata$percent
fulldata$percent <- coalesce(fixpercent, 0)
fulldata <- fulldata %>%
  spread(aoi, percent)

fulldata %>%
  filter(eye_exclude == FALSE) %>%
  select(direction, belly:upperchest) %>%
  gather(aoi, percent, belly:upperchest) %>%
  ggplot(aes(x = aoi, y = percent, fill = direction)) + geom_boxplot()
```

But let's try error charts too! Instead of boxplots.

```{r eye gaze error bar chart}
fulldata_error <- fulldata %>%
  filter(eye_exclude == FALSE) %>%
  gather(aoi, percent, belly:upperchest) %>%
  group_by(id, direction, aoi) %>%
  summarise(percent = mean(percent, na.rm = TRUE)) %>%
  ungroup() %>%
  distinct() %>%
  group_by(direction, aoi) %>%
  summarise(mean = mean(percent, na.rm = TRUE),
            sd = sd(percent, na.rm = TRUE),
            count = n(),
            se = sd/sqrt(count))
fulldata_error$aoi <- fct_relevel(fulldata_error$aoi, c("forehead","eyes","mouth","neck","upperchest",
                                                        "midchest","lowerchest","belly","left","right"))

fulldata_error %>%
  ggplot(aes(x = aoi, y = mean, fill = direction)) + 
  geom_bar(stat = "identity", position = position_dodge()) +
  geom_errorbar(aes(ymin = mean-se, ymax = mean+se), position = position_dodge(0.9), width = 0.5) +
  labs(title = "Eye Gaze Behavior", x = "", y = "looking time") +
  scale_y_continuous(labels = percent, limits = c(0,.70)) + 
  theme_bw() +
  theme(axis.text.x = element_text(angle = 30, hjust = 1, vjust = 1))
```

And a table of eye gaze results too

```{r eye gaze error table}
fulldata_gazetable <- fulldata %>%
  filter(eye_exclude == FALSE) %>%
  gather(aoi, percent, belly:upperchest) %>%
  group_by(id, maingroup, direction, aoi) %>%
  summarise(percent = mean(percent, na.rm = TRUE)) %>%
  ungroup() %>%
  distinct() %>%
  group_by(maingroup, direction, aoi) %>%
  summarise(mean = mean(percent, na.rm = TRUE),
            sd = sd(percent, na.rm = TRUE)) %>%
  mutate(mean = round(mean*100,1),
         sd = round(sd*100,1)) %>%
  mutate(value = paste(mean, sd, sep = " ± ")) %>%
  mutate(value = paste(value, "%", sep = ""))

fulldata_gazetable$aoi <- fct_relevel(fulldata_gazetable$aoi, c("forehead","eyes","mouth","neck","upperchest",
                                                        "midchest","lowerchest","belly","left","right"))

fulldata_gazetable %>% 
  ungroup() %>%
  select(-mean, -sd) %>% 
  spread(aoi, value)

fulldata_total <- fulldata %>%
  filter(eye_exclude == FALSE) %>%
  gather(aoi, percent, belly:upperchest) %>%
  group_by(aoi) %>%
  summarise(percent = mean(percent, na.rm = TRUE)) %>%
  spread(aoi, percent)

#sum(fulldata_total$eyes, fulldata_total$mouth, fulldata_total$neck)
#fulldata_total$left
#fulldata_total$right
```
## Big Three-Way ANOVA
Now we're going to try a three-way Group x Direction x AOI ANOVA with the top 3 AOIs (Eyes, Mouth, Neck)

```{r big anova aoi3, echo=FALSE, message=FALSE, warning=FALSE, results="markup"}
aoi3 <- c('eyes','mouth','neck')

# Organize the data by those AOIs (but again we need participant-level first)
aoi3_data <- fulldata %>%
  filter(eye_exclude == FALSE) %>%
  select(id, maingroup, hearing, aoasl, age, direction, belly:upperchest) %>%
  gather(aoi, percent, belly:upperchest) %>%
  group_by(id, direction, aoi) %>%
  mutate(percent = mean(percent, na.rm = TRUE)) %>%
  ungroup() %>%
  distinct() %>%
  filter(aoi %in% aoi3) %>%
  spread(aoi, percent)

# # Limit to eyes mouth neck
# big_aov_aoi3 <- aoi3_data %>%
#   gather(aoi, percent, eyes:neck) %>%
#   aov(percent ~ maingroup * direction * aoi, data = .)
# 
# summary(big_aov_aoi3)
# 
# big_aov_aoi3_lsd <- LSD.test(big_aov_aoi3, "aoi", group = FALSE)
# big_aov_aoi3_lsd$comparison

aoi3_data_gather <- aoi3_data %>%
  gather(aoi, percent, eyes:neck) %>%
  filter(id != "6")

big3_aov <- ezANOVA(
  data = aoi3_data_gather,
  dv = percent,
  wid = id,
  within = .(direction, aoi),
  between = maingroup,
  type = 3
)["ANOVA"]

print(big3_aov["ANOVA"])
```
## Left Vs Right
```{r left right anova, echo=FALSE, message=FALSE, warning=FALSE, results="markup"}
aoi3 <- c('left','right')

# Organize the data by those AOIs (but again we need participant-level first)
aoiLR_data <- fulldata %>%
  filter(eye_exclude == FALSE) %>%
  select(id, maingroup, hearing, aoasl, age, direction, belly:upperchest) %>%
  gather(aoi, percent, belly:upperchest) %>%
  group_by(id, aoi) %>%
  mutate(percent = mean(percent, na.rm = TRUE)) %>%
  ungroup() %>%
  distinct() %>%
  filter(aoi %in% aoi3) %>%
  spread(aoi, percent)

aoiLR_data_gather <- aoiLR_data %>%
  gather(aoi, percent, left:right) %>%
  filter(id != "6")

bigLR_aov <- ezANOVA(
  data = aoiLR_data_gather,
  dv = percent,
  wid = id,
  within = aoi,
  between = maingroup,
  type = 3
)["ANOVA"]

print(bigLR_aov["ANOVA"])
```

## Interactions Visualization
So we have significant maingroup:aoi and direction:aoi interactions. Let's try to visualize what can be driving these. We can go back to the SEM chart but break it down...

First are the maingroup:aoi charts *The error bars are not 100% accurate, I took a quick-n-easy way around*

```{r}
aoi3_interactions_maingroupaoi <- fulldata %>%
  filter(eye_exclude == FALSE) %>%
  select(id, participant, maingroup, direction, eyes, mouth, neck) %>%
  gather(aoi, percent, c(eyes, mouth, neck)) %>%
  group_by(id, maingroup, direction, aoi) %>%
  mutate(percent = mean(percent, na.rm = TRUE)) %>%
  distinct() %>%
  group_by(maingroup, aoi) %>%
  summarise(mean = mean(percent, na.rm = TRUE),
            sd = sd(percent, na.rm = TRUE),
            count = n()/2,
            se = sd/sqrt(count))
# I need to first collapse across stories for each participant...here I didn't. Must fix later.

aoi3_interactions_maingroupaoi %>% 
  ggplot(aes(x = aoi, y = mean, fill = maingroup)) + 
  geom_bar(stat = "identity", position = position_dodge()) +
  geom_errorbar(aes(ymin = mean-se, ymax = mean+se), position = position_dodge(0.9), width = 0.5) +
  labs(title = "MainGroup & AOI Interaction 1", subtitle = "Error bars represent SE", x = "", y = "percent looking") +
  scale_y_continuous(labels = percent)

aoi3_interactions_maingroupaoi %>% 
  ggplot(aes(x = maingroup, y = mean, fill = aoi)) + 
  geom_bar(stat = "identity", position = position_dodge()) +
  geom_errorbar(aes(ymin = mean-se, ymax = mean+se), position = position_dodge(0.9), width = 0.5) +
  labs(title = "MainGroup & AOI Interaction 2", subtitle = "Error bars represent SE", x = "", y = "percent looking") +
  scale_y_continuous(labels = percent)


```

First are the direction:aoi charts *The error bars are not 100% accurate, I took a quick-n-easy way around*

```{r}
aoi3_interactions_directionaoi <- fulldata %>%
  filter(eye_exclude == FALSE) %>%
  select(id, participant, maingroup, direction, eyes, mouth, neck) %>%
  gather(aoi, percent, c(eyes, mouth, neck)) %>%
  group_by(id, maingroup, direction, aoi) %>%
  mutate(percent = mean(percent, na.rm = TRUE)) %>%
  distinct() %>%
  group_by(direction, aoi) %>%
  summarise(mean = mean(percent, na.rm = TRUE),
            sd = sd(percent, na.rm = TRUE),
            count = n(),
            se = sd/sqrt(count))
# I need to first collapse across stories for each participant...here I didn't. Must fix later.

aoi3_interactions_directionaoi %>% 
  ggplot(aes(x = aoi, y = mean, fill = direction)) + 
  geom_bar(stat = "identity", position = position_dodge()) +
  geom_errorbar(aes(ymin = mean-se, ymax = mean+se), position = position_dodge(0.9), width = 0.5) +
  labs(title = "MainGroup & Direction Interaction 1", subtitle = "Error bars represent SE", x = "", y = "percent looking") +
  scale_y_continuous(labels = percent)

aoi3_interactions_directionaoi %>% 
  ggplot(aes(x = direction, y = mean, fill = aoi)) + 
  geom_bar(stat = "identity", position = position_dodge()) +
  geom_errorbar(aes(ymin = mean-se, ymax = mean+se), position = position_dodge(0.9), width = 0.5) +
  labs(title = "MainGroup & Direction Interaction 2", subtitle = "Error bars represent SE", x = "", y = "percent looking") +
  scale_y_continuous(labels = percent)
```


## All the AOI means
Let's get tables of our means here, in various configurations

```{r aoi raw, echo = FALSE, results = 'markup'}
aoi3_data %>%
  group_by(maingroup, direction) %>%
  summarise(eyes = mean(eyes, na.rm = TRUE),
            mouth = mean(mouth, na.rm = TRUE),
            neck = mean(neck, na.rm = TRUE)) %>%
  rowwise() %>%
  mutate(total = sum(eyes,mouth,neck))

aoi3_data %>%
  group_by(maingroup, direction) %>%
  summarise(eyes = mean(eyes, na.rm = TRUE),
            mouth = mean(mouth, na.rm = TRUE),
            neck = mean(neck, na.rm = TRUE)) %>%
  group_by(maingroup) %>%
  summarise(eyes = mean(eyes, na.rm = TRUE),
            mouth = mean(mouth, na.rm = TRUE),
            neck = mean(neck, na.rm = TRUE))  %>%
  rowwise() %>%
  mutate(total = sum(eyes,mouth,neck))

aoi3_data %>%
  group_by(maingroup, direction) %>%
  summarise(eyes = mean(eyes, na.rm = TRUE),
            mouth = mean(mouth, na.rm = TRUE),
            neck = mean(neck, na.rm = TRUE)) %>%
  group_by(direction) %>%
  summarise(eyes = mean(eyes, na.rm = TRUE),
            mouth = mean(mouth, na.rm = TRUE),
            neck = mean(neck, na.rm = TRUE)) %>%
  rowwise() %>%
  mutate(total = sum(eyes,mouth,neck))

aoi3_data %>%
  group_by(maingroup, direction) %>%
  summarise(eyes = mean(eyes, na.rm = TRUE),
            mouth = mean(mouth, na.rm = TRUE),
            neck = mean(neck, na.rm = TRUE)) %>%
  group_by(maingroup) %>%
  summarise(eyes = mean(eyes, na.rm = TRUE),
            mouth = mean(mouth, na.rm = TRUE),
            neck = mean(neck, na.rm = TRUE)) %>%
  ungroup() %>% 
  gather(aoi, percent, eyes:neck) %>%
  group_by(aoi) %>%
  summarise(percent = mean(percent, na.rm = TRUE))
```


## Eyes

1. ANOVA with factors MainGroup & Direction.

```{r eyes anova1, echo=FALSE, results = 'markup'}

# # eyes ANCOVA 1
# eyes_aov1 <- aov(eyes ~ maingroup * direction, data = aoi3_data)
# summary(eyes_aov1)
# # eyes_lsd1 <- LSD.test(eyes_aov1, "maingroup", group = FALSE)
# # eyes_lsd1$comparison

aoi3_data <- filter(aoi3_data, id != "6")
ezANOVA(
  data = aoi3_data,
  dv = eyes,
  wid = id,
  within = direction,
  between = maingroup,
  type = 3
)["ANOVA"]
```

2. ANOVA with factor MainGroup, for Forward only.

```{r eyes anova2, echo=FALSE, results = 'markup'}
# # eyes ANOVA 2
# eyes_aov2 <- aov(eyes ~ maingroup, data = filter(aoi3_data, direction == "forward"))
# summary(eyes_aov2)
# # eyes_lsd2 <- LSD.test(eyes_aov2, "maingroup", group = FALSE)
# # eyes_lsd2$comparison

ezANOVA(
  data = filter(aoi3_data, direction == "forward"),
  dv = eyes,
  wid = id,
  between = maingroup,
  type = 3
)["ANOVA"]
```

3. ANOVA with factor MainGroup, for Reverse only. 

```{r eyes anova3, echo=FALSE, results = 'markup'}
# # eyes ANOVA 3
# eyes_aov3 <- aov(eyes ~ maingroup, data = filter(aoi3_data, direction == "reversed"))
# summary(eyes_aov3)
# # eyes_lsd3 <- LSD.test(eyes_aov3, "maingroup", group = FALSE)
# # eyes_lsd3$comparison

ezANOVA(
  data = filter(aoi3_data, direction == "reversed"),
  dv = eyes,
  wid = id,
  between = maingroup,
  type = 3
)["ANOVA"]
```

## Mouth

1. ANOVA with factors MainGroup & Direction.

```{r mouth anova1, echo=FALSE, results = 'markup'}

# # mouth ANCOVA 1
# mouth_aov1 <- aov(mouth ~ maingroup * direction, data = aoi3_data)
# summary(mouth_aov1)
# mouth_lsd1 <- LSD.test(mouth_aov1, "maingroup", group = FALSE)
# mouth_lsd1$comparison

ezANOVA(
  data = aoi3_data,
  dv = mouth,
  wid = id,
  within = direction,
  between = maingroup,
  type = 3
)["ANOVA"]
```

2. ANOVA with factor MainGroup, for Forward only.

```{r mouth anova2, echo=FALSE, results = 'markup'}
# # mouth ANOVA 2
# mouth_aov2 <- aov(mouth ~ maingroup, data = filter(aoi3_data, direction == "forward"))
# summary(mouth_aov2)
# # mouth_lsd2 <- LSD.test(mouth_aov2, "maingroup", group = FALSE)
# # mouth_lsd2$comparison
ezANOVA(
  data = filter(aoi3_data, direction == "forward"),
  dv = mouth,
  wid = id,
  between = maingroup,
  type = 3
)["ANOVA"]
```

3. ANOVA with factor MainGroup, for Reverse only. 

```{r mouth anova3, echo=FALSE, results = 'markup'}
# # mouth ANOVA 3
# mouth_aov3 <- aov(mouth ~ maingroup, data = filter(aoi3_data, direction == "reversed"))
# summary(mouth_aov3)
# # mouth_lsd3 <- LSD.test(mouth_aov3, "maingroup", group = FALSE)
# # mouth_lsd3$comparison
ezANOVA(
  data = filter(aoi3_data, direction == "reversed"),
  dv = mouth,
  wid = id,
  between = maingroup,
  type = 3
)["ANOVA"]
```

## Neck

1. ANOVA with factors MainGroup & Direction.

```{r neck anova1, echo=FALSE, results = 'markup'}

# # neck ANCOVA 1
# neck_aov1 <- aov(neck ~ maingroup * direction, data = aoi3_data)
# summary(neck_aov1)
# # neck_lsd1 <- LSD.test(neck_aov1, "maingroup", group = FALSE)
# # neck_lsd1$comparison

ezANOVA(
  data = aoi3_data,
  dv = neck,
  wid = id,
  within = direction,
  between = maingroup,
  type = 3
)["ANOVA"]
```

2. ANOVA with factor MainGroup, for Forward only.

```{r neck anova2, echo=FALSE, results = 'markup'}
# # neck ANOVA 2
# neck_aov2 <- aov(neck ~ maingroup, data = filter(aoi3_data, direction == "forward"))
# summary(neck_aov2)
# # neck_lsd2 <- LSD.test(neck_aov2, "maingroup", group = FALSE)
# # neck_lsd2$comparison
ezANOVA(
  data = filter(aoi3_data, direction == "forward"),
  dv = neck,
  wid = id,
  between = maingroup,
  type = 3
)["ANOVA"]
```

3. ANOVA with factor MainGroup, for Reverse only. 

```{r neck anova3, echo=FALSE, results = 'markup'}
# # neck ANOVA 3
# neck_aov3 <- aov(neck ~ maingroup, data = filter(aoi3_data, direction == "reversed"))
# summary(neck_aov3)
# # neck_lsd3 <- LSD.test(neck_aov3, "maingroup", group = FALSE)
# # neck_lsd3$comparison
ezANOVA(
  data = filter(aoi3_data, direction == "reversed"),
  dv = neck,
  wid = id,
  between = maingroup,
  type = 3
)["ANOVA"]
```

## FaceChest

We originally defined FaceChest such that

1. Face = eyes + mouth + chin
1. Chest = upperchest + midchest + lowerchest

BUT. Chin is actually neck. It's not even part of the face if you think about it. So I'm redefining FaceChest as:

1. Face = forehead + eyes + mouth
1. Chest = neck + upperchest + midchest + lowerchest

So let's do this. Then see what's happening across groups for FaceChest.

```{r calculate facechest, echo=FALSE, results = 'markup'}
# Calculate face, chest, and facechest - and moutheye too
fulldata <- fulldata %>%
  rowwise() %>%
  mutate(face = sum(forehead, eyes, mouth, na.rm = TRUE),
         chest = sum(neck, upperchest, midchest, lowerchest, na.rm = TRUE),
         facechest = (face - chest)/(face + chest),
         moutheye = (mouth - eyes)/(mouth + eyes))

# fulldata %>% 
#   gather(metric, value, c(facechest_old, facechest)) %>%
#   ggplot(aes(x = maingroup, y = value, fill = direction)) + geom_boxplot() + facet_wrap("metric")
```

Cool. Next we'll do error bar charts using the new FaceChest across groups. 

```{r facechest bars, message=FALSE, warning=FALSE}
facechest_info <- fulldata %>%
  filter(eye_exclude == FALSE) %>%
  group_by(maingroup, direction, participant) %>%
  summarise(facechest = mean(facechest, na.rm = TRUE)) %>%
  group_by(maingroup, direction) %>%
  summarise(mean = mean(facechest),
            sd = sd(facechest),
            n = n(),
            se = sd/sqrt(n)) %>%
  ungroup() %>%
  mutate(maingroup = case_when(
    maingroup == "DeafEarly" ~ "Deaf Early",
    maingroup == "DeafLate" ~ "Deaf Late",
    maingroup == "HearingLate" ~ "Hearing Late",
    maingroup == "HearingNovice" ~ "Hearing Novice"
    ))

ggplot(facechest_info, aes(x = maingroup, y = mean, fill = direction, color = direction)) +
  geom_point(stat = "identity", position = position_dodge(0.5), size = 2) + 
  geom_errorbar(aes(ymin = mean-se, ymax = mean+se), position = position_dodge(0.5), width = 0.3, size = 1) +
  labs(title = "Face-Chest Ratio", x = "", y = "face-chest ratio") +
  scale_y_continuous(limits = c(-1,1)) + 
  geom_hline(yintercept = 0, linetype = "dotted") +
  theme_bw()
```

Now let's do the ANOVAs. Also skipping LSDs here. 

1. ANOVA with factors MainGroup & Direction.

```{r facechest anova 1, echo=FALSE, results = 'markup', message=FALSE}
# Create the participant-level db (let's also pop moutheye in this too)
fc_data <- fulldata %>%
  filter(eye_exclude == FALSE) %>%
  select(id, maingroup, hearing, aoasl, age, direction, facechest, moutheye) %>%
  group_by(id, direction) %>%
  mutate(facechest = mean(facechest, na.rm = TRUE),
         moutheye = mean(moutheye, na.rm = TRUE)) %>%
  ungroup() %>%
  distinct() %>%
  filter(id != "6")

# # facechest ANCOVA 1
# facechest_aov1 <- aov(facechest ~ maingroup * direction, data = fc_data)
# summary(facechest_aov1)
# # facechest_lsd1 <- LSD.test(facechest_aov1, "maingroup", group = FALSE)
# # facechest_lsd1$comparison

ezANOVA(
  data = fc_data,
  dv = facechest,
  wid = id,
  within = direction,
  between = maingroup,
  type = 3
)["ANOVA"]
```

2. ANOVA with factor MainGroup, for Forward only.

```{r facechest anova2, echo=FALSE, results = 'markup'}
# # facechest ANOVA 2
# facechest_aov2 <- aov(facechest ~ maingroup, data = filter(fc_data, direction == "forward"))
# summary(facechest_aov2)
# # facechest_lsd2 <- LSD.test(facechest_aov2, "maingroup", group = FALSE)
# # facechest_lsd2$comparison

ezANOVA(
  data = filter(fc_data, direction == "forward"),
  dv = facechest,
  wid = id,
  between = maingroup,
  type = 3
)["ANOVA"]
```

3. ANOVA with factor MainGroup, for Reverse only. 

```{r facechest anova3, echo=FALSE, results = 'markup'}
# # facechest ANOVA 3
# facechest_aov3 <- aov(facechest ~ maingroup, data = filter(fc_data, direction == "reversed"))
# summary(facechest_aov3)
# # facechest_lsd3 <- LSD.test(facechest_aov3, "maingroup", group = FALSE)
# # facechest_lsd3$comparison
ezANOVA(
  data = filter(fc_data, direction == "reversed"),
  dv = facechest,
  wid = id,
  between = maingroup,
  type = 3
)["ANOVA"]
```

# Eye Gaze & Performance Correlations

Next we're going to correlate our eye gaze metrics (Eye, Mouth, Neck, and FaceChest) with lexical recall and gist. Okay! But remember we have a slightly smaller dataset here because we've excluded some participants for having bad eye data (but they had valid behavioral data so we kept them in the AoA-Performance correlations above). 

**IMPORTANT: We also removed gist_forward column.**

```{r repackage data for correlations again}
eyeperf <- fulldata %>%
  filter(eye_exclude == FALSE) %>%
  select(participant, maingroup, hearing, direction, aoasl, signyrs, selfrate, acc, gist, eyes, mouth, neck, facechest) %>%
  group_by(maingroup, participant, direction) %>%
  mutate(gist = mean(gist, na.rm = TRUE),
         lex = mean(acc, na.rm = TRUE),
         eyes = mean(eyes, na.rm = TRUE),
         mouth = mean(mouth, na.rm = TRUE),
         neck = mean(neck, na.rm = TRUE),
         facechest = mean(facechest, na.rm = TRUE)) %>%
  ungroup() %>%
  select(maingroup, participant, hearing, direction, aoasl, signyrs, selfrate, gist, lex, eyes, mouth, neck, facechest) %>%
  distinct() %>%
  gather(metric, value, gist:facechest) %>%
  unite(metricvalue, c(metric, direction), sep = "_") %>%
  spread(metricvalue, value) %>%
  select(-participant, -maingroup) %>%
  select(hearing, aoasl, signyrs, selfrate, gist_reversed, lex_forward, lex_reversed, eyes_forward,
         eyes_reversed, mouth_forward, mouth_reversed, neck_forward, neck_reversed, 
         facechest_forward, facechest_reversed)

eyeperf_deaf <- eyeperf %>% filter(hearing == "Deaf") %>% select(-hearing)
eyeperf_deaf_fw <- eyeperf_deaf %>% select(aoasl, signyrs, selfrate, lex_forward, eyes_forward,
                                           mouth_forward, neck_forward, facechest_forward)
eyeperf_deaf_rv <- eyeperf_deaf %>% select(aoasl, signyrs, selfrate, gist_reversed, lex_reversed,
                                           eyes_reversed, mouth_reversed, neck_reversed, facechest_reversed)
eyeperf_hearing <- eyeperf %>% filter(hearing == "Hearing") %>% select(-hearing)
eyeperf_hearing_fw <- eyeperf_hearing %>% select(aoasl, signyrs, selfrate, lex_forward, eyes_forward,
                                           mouth_forward, neck_forward, facechest_forward)
eyeperf_hearing_rv <- eyeperf_hearing %>% select(aoasl, signyrs, selfrate, gist_reversed, lex_reversed,
                                           eyes_reversed, mouth_reversed, neck_reversed, facechest_reversed)
eyeperf_all <- eyeperf %>% select(-hearing)
eyeperf_all_fw <- eyeperf_all %>% select(aoasl, signyrs, selfrate, lex_forward, eyes_forward,
                                           mouth_forward, neck_forward, facechest_forward)
eyeperf_all_rv <- eyeperf_all %>% select(aoasl, signyrs, selfrate, gist_reversed, lex_reversed,
                                           eyes_reversed, mouth_reversed, neck_reversed, facechest_reversed)

```
## Deaf Correlations, Forward
```{r rows.print = 15}
# Correlations for Deaf
print("DEAF Correlations - Pearson's r")
#corstarsl(lexgist_deaf)
Hmisc::rcorr(as.matrix(eyeperf_deaf_fw))$r
print("DEAF Correlations - P-values")
Hmisc::rcorr(as.matrix(eyeperf_deaf_fw))$P
cat(paste("","\n",""))
corstarsl(eyeperf_deaf_fw)
```
## Deaf Correlations, Reversed
```{r rows.print = 15}
# Correlations for Deaf
print("DEAF Correlations - Pearson's r")
#corstarsl(lexgist_deaf)
Hmisc::rcorr(as.matrix(eyeperf_deaf_rv))$r
print("DEAF Correlations - P-values")
Hmisc::rcorr(as.matrix(eyeperf_deaf_rv))$P
cat(paste("","\n",""))
corstarsl(eyeperf_deaf_rv)
```
## Deaf Correlations, Both Directions
```{r rows.print = 15}
# Correlations for Deaf
print("DEAF Correlations - Pearson's r")
#corstarsl(lexgist_deaf)
Hmisc::rcorr(as.matrix(eyeperf_deaf))$r
print("DEAF Correlations - P-values")
Hmisc::rcorr(as.matrix(eyeperf_deaf))$P
cat(paste("","\n",""))
corstarsl(eyeperf_deaf)
```

## Hearing Correlations, Forward
```{r rows.print = 15}
# Correlations for Hearing
print("HEARING Correlations - Pearson's r")
#corstarsl(lexgist_hearing)
Hmisc::rcorr(as.matrix(eyeperf_hearing_fw))$r
print("HEARING Correlations - P-values")
Hmisc::rcorr(as.matrix(eyeperf_hearing_fw))$P
cat(paste("","\n",""))
corstarsl(eyeperf_hearing_fw)
```

## Hearing Correlations, Reversed
```{r rows.print = 15}
# Correlations for Hearing
print("HEARING Correlations - Pearson's r")
#corstarsl(lexgist_hearing)
Hmisc::rcorr(as.matrix(eyeperf_hearing_rv))$r
print("HEARING Correlations - P-values")
Hmisc::rcorr(as.matrix(eyeperf_hearing_rv))$P
cat(paste("","\n",""))
corstarsl(eyeperf_hearing_rv)
```

## Hearing Correlations, Both Directions
```{r rows.print = 15}
# Correlations for Hearing
print("HEARING Correlations - Pearson's r")
#corstarsl(lexgist_hearing)
Hmisc::rcorr(as.matrix(eyeperf_hearing))$r
print("HEARING Correlations - P-values")
Hmisc::rcorr(as.matrix(eyeperf_hearing))$P
cat(paste("","\n",""))
corstarsl(eyeperf_hearing)
```

## All People Correlations, Forward
```{r correlations for eyeperffwrv, rows.print=15}

eyeperf_fwrv <- fulldata %>%
  filter(eye_exclude == FALSE) %>%
  select(participant, maingroup, hearing, direction, aoasl, signyrs, selfrate, acc, gist, eyes, mouth, neck, facechest) %>%
  group_by(maingroup, participant, direction) %>%
  mutate(gist = mean(gist, na.rm = TRUE),
         lex = mean(acc, na.rm = TRUE),
         eyes = mean(eyes, na.rm = TRUE),
         mouth = mean(mouth, na.rm = TRUE),
         neck = mean(neck, na.rm = TRUE),
         facechest = mean(facechest, na.rm = TRUE)) %>%
  ungroup() %>%
  select(maingroup, participant, hearing, direction, aoasl, signyrs, selfrate, gist, lex, eyes, mouth, neck, facechest) %>%
  distinct() %>%
  gather(metric, value, gist:facechest) %>%
  unite(metricvalue, c(metric, direction), sep = "_", remove = FALSE) %>%
  select(-metric) %>%
  spread(metricvalue, value) %>%
  select(-participant, -maingroup, -hearing) %>%
  select(direction, aoasl, signyrs, selfrate, gist_reversed, lex_forward, lex_reversed, eyes_forward, eyes_reversed,
         mouth_forward, mouth_reversed, neck_forward, neck_reversed, facechest_forward, facechest_reversed)

eyeperf_fw <- eyeperf_fwrv %>% filter(direction == "forward") %>% 
  select(-direction, -gist_reversed, -lex_reversed, -eyes_reversed, -mouth_reversed, -neck_reversed, -facechest_reversed)
eyeperf_rv <- eyeperf_fwrv %>% filter(direction == "reversed") %>% select(-direction, -lex_forward, -eyes_forward, -mouth_forward, -neck_forward, -facechest_forward)

# Correlations for FW
print("FW Correlations - Pearson's r")
#corstarsl(lexgist_deaf)
Hmisc::rcorr(as.matrix(eyeperf_fw))$r
print("FW Correlations - P-values")
Hmisc::rcorr(as.matrix(eyeperf_fw))$P
cat(paste("","\n",""))
corstarsl(eyeperf_fw)
```

## All People Correlations, Reversed
```{r correlations for eyeperfrv, rows.print=15}
# Correlations for RV
print("RV Correlations - Pearson's r")
#corstarsl(lexgist_hearing)
Hmisc::rcorr(as.matrix(eyeperf_rv))$r
print("RV Correlations - P-values")
Hmisc::rcorr(as.matrix(eyeperf_rv))$P
cat(paste("","\n",""))
corstarsl(eyeperf_rv)
```

## All People Correlations, Both Directions
```{r rows.print = 15}
# Correlations for All
print("ALL Correlations - Pearson's r")
#corstarsl(lexgist_all)
Hmisc::rcorr(as.matrix(eyeperf_all))$r
print("ALL Correlations - P-values")
Hmisc::rcorr(as.matrix(eyeperf_all))$P
corstarsl(eyeperf_all)
```

## Correlation Table
And the correlation table.

```{r eyeperf correlation charts, fig.height=12, fig.width=12, message=FALSE, warning=FALSE}
ggpairs(eyeperf, columns = c(2:13), aes(color = hearing))
```

# Heat Maps
And finally, we're going to do heat maps. 
```{r heat map}
eyegaze_heat <- fulldata %>%
  ungroup() %>%
  filter(eye_exclude == FALSE) %>%
  select(id:direction, belly, lowerchest, midchest, upperchest, neck, mouth, eyes, forehead) %>%
  gather(aoi, percent, belly:forehead) %>%
  group_by(maingroup, participant, direction, aoi) %>%
  summarise(percent = mean(percent, na.rm=TRUE)) %>%
  group_by(maingroup,direction,aoi) %>%
  summarise(percent = mean(percent, na.rm=TRUE)) %>%
  ungroup() %>%
  filter(!is.na(aoi)) %>%
  mutate(aoi = factor(aoi,levels=c("belly","lowerchest","midchest",
                                   "upperchest","neck","mouth","eyes","forehead"))) %>%
  ungroup() %>%
  mutate(maingroup = case_when(
    maingroup == "DeafEarly" ~ "Deaf Early",
    maingroup == "DeafLate" ~ "Deaf Late",
    maingroup == "HearingLate" ~ "Hearing Late",
    maingroup == "HearingNovice" ~ "Hearing Novice"
    ))

eyegaze_heat_all <- fulldata %>%
  ungroup() %>%
  filter(eye_exclude == FALSE) %>%
  select(id:direction, belly, lowerchest, midchest, upperchest, neck, mouth, eyes, forehead) %>%
  gather(aoi, percent, belly:forehead) %>%
  group_by(maingroup,participant,direction,aoi) %>%
  dplyr::summarize(percent = mean(percent, na.rm=TRUE)) %>%
  group_by(maingroup,direction,aoi) %>%
  dplyr::summarize(percent = mean(percent, na.rm=TRUE)) %>%
  group_by(maingroup,aoi) %>%
  dplyr::summarize(percent = mean(percent, na.rm=TRUE)) %>%
  ungroup() %>%
  filter(!is.na(aoi)) %>%
  mutate(aoi = factor(aoi,levels=c("belly","lowerchest","midchest",
                                   "upperchest","neck","mouth","eyes","forehead"))) %>%
  ungroup() %>%
  mutate(maingroup = case_when(
    maingroup == "DeafEarly" ~ "Deaf Early",
    maingroup == "DeafLate" ~ "Deaf Late",
    maingroup == "HearingLate" ~ "Hearing Late",
    maingroup == "HearingNovice" ~ "Hearing Novice"
    ))

ggplot(eyegaze_heat, aes(x = maingroup, y = aoi)) +
  geom_tile(aes(fill=percent),color="lightgray",na.rm=TRUE) + 
  scale_fill_viridis(option = "viridis", direction=-1, limits = c(0,.71), labels = percent, name = "looking time") +
  theme_bw() +
  theme(axis.text.x=element_text(angle=30,hjust=1),
        strip.text.x = element_text(size = 11, color = "black", face = "italic"), 
        strip.background = element_rect(colour = "white", fill = "white"),
        panel.grid.major = element_line(color = "white")) +
  facet_grid(. ~ direction) +
  ylab("") + xlab("") + ggtitle("Eye Gaze Heat Map, by Direction") + 
  scale_y_discrete(expand=c(0,0)) +
  scale_x_discrete(expand = c(0,0))


ggplot(eyegaze_heat_all, aes(x = maingroup, y = aoi)) +
  geom_tile(aes(fill=percent),color="lightgray",na.rm=TRUE) + 
  scale_fill_viridis(option = "viridis", direction=-1, limits = c(0,.71), labels = percent, name = "looking time") +
  theme_bw() +
  theme(axis.text.x=element_text(angle=30,hjust=1), 
  panel.grid.major = element_line(color = "white")) +
  ylab("") + xlab("") + ggtitle("Eye Gaze Heat Map (Direction Collapsed)") +
  scale_y_discrete(expand=c(0,0)) +
  scale_x_discrete(expand = c(0,0))

```

# Summary

Below are the p-values from the ANOVAs with 4 MainGroups. I never included Age as a covariate because it never improved the model. I included all ANOVAs for Gist and Lex Recall, and ANOVAs for any eye AOI or ratio was included only if either maingroup or direction was significant. Deafearly-Deaflate shows the LSD p-value for that comparison.
```{r results1, rows.print = 20}
results1 <- structure(list(model = c("gist-maingroup-both", "gist-maingroup-fw", 
"gist-maingroup-rv", "lexrecall-maingroup-both", "lexrecall-maingroup-fw", 
"lexrecall-maingroup-rv", "mouth-maingroup-both", "upperchest-maingroup-both", 
"upperchest-maingroup-rv", "facechest-maingroup-both", "moutheye-maingroup-both"
), maingroup = c(0, 0, 0.01, 0, 0.04, 0.02, 0.06, 0, 0.01, 0.1, 
0.05), direction = c(0, NA, NA, 0, NA, NA, 0.06, 0.16, NA, 0.07, 
0.48), `deafearly-deaflate` = c(0.1, 0.69, 0.02, 0.11, 0.95, 
0.06, 0.38, 0.94, 0.52, 0.08, 0.68)), .Names = c("model", "maingroup", 
"direction", "deafearly-deaflate"), class = c("tbl_df", "tbl", 
"data.frame"), row.names = c(NA, -11L))

results1
```

And below are the p-values from the ANCOVAs with Hearing & AoASL. I included all ANCOVAs for Gist and Lex Recall, and ANCOVAs for any eye AOI or ratio was included only if any main factor was significant. LSD comparisons are not needed because there's only 2 levels in each group!

```{r results2, rows.print = 25}
results2 <- structure(list(model = c("gist-both", "gist-fw", "gist-rv", "lex-both", 
"lex-fw", "lex-rv", "forehead-fw", "mouth-both", "mouth-rv", 
"upperchest-both", "upperchest-rv", "facechest-both", "moutheye-both"
), hearing = c(0, 0.00, 0.01, 0.01, 0.22, 0.03, 0.06, 0.01, 
0.04, 0.01, 0.01, 0.35, 0.07), direction = c(0, NA, NA, 0, NA, 
NA, NA, 0.05, NA, 0.21, NA, 0.05, 0.52), aoasl = c(0.22, 0.77, 
0.19, 0.56, 0.58, 0.25, 0.08, 0.06, 0.12, 0.68, 0.95, 0.12, 0.44
), age = c(0.08, 0.01, 0.86, 0.09, 0.02, 0.7, 0.68, 0.28, 0.5, 
0.02, 0.08, 0.00, 0.21)), .Names = c("model", "hearing", "direction", 
"aoasl", "age"), class = c("tbl_df", "tbl", "data.frame"), row.names = c(NA, 
-13L))
results2
```

Finally, the correlations for Deaf and Hearing separately are not significant. But there are significant correlations across all participants. I worry it is caused by HearingNovice, though...

```{r correlation table}
results3 <- tribble(
  ~ metric, ~ AoASLcorrelationRvalue, ~ Pvalue,
  "gist-fw", -0.32, 0.019,
  "gist-rv", -0.39, 0.004,
  "lex-fw", -0.08, 0.567,
  "lex-rv", -0.34, 0.014
)
results3
```

# Ternary Plots

Let's make triangle plots. "What?" you say. Read on. 

```{r fig.height=12, fig.width=12, message=FALSE, warning=FALSE}
library(ggtern)
fulldata %>% 
  ggtern(aes(x = eyes, y = mouth, z = neck)) + facet_grid(direction ~ maingroup) + stat_density_tern(geom='polygon', aes(fill=..level..), bins=4) + geom_point(color = "white", alpha = 0.5) + theme_bw()

fulldata %>% 
  ggtern(aes(x = eyes, y = mouth, z = neck)) + facet_grid(direction ~ maingroup) + geom_confidence_tern(breaks = c(.5), color = "red") + geom_point() + theme_bw()
```

# Rain's Notes
About Adults:

-I think I want to write it up as an ANCOVA, with direction included.  And LSD comparisons instead of Tukey.  (I will do my own corrections)
-You often have one liners summarizing results, in all tabs, those are nice, keep them coming.
-(If you have reasons to present anything other than the ANCOVA, put that in your results tab)
 
I think if we do it this way then we get a really important story to tell:  That the *critical* AoA cutoff is below 4 vs above 4 years of age (two groups 0-4 vs 4-13).  This suggest early ASL is important.  

```{r eval=FALSE, include=FALSE}
# Gist as binomial logit-link function model
gist_glmm <- glmer(gist ~ direction * maingroup + (1|id) + (1|story), data = fulldata, family=binomial (link="logit"))
summary(gist_glmm)
```

# Viewing Space Correlations 

Okay now let's work with the raw viewing space that we created in [05viewingspace](05viewingspace.nb.html). For each participant the first 30 samples was removed. 

+ box width
+ upper limit
+ lower limit
+ left side
+ right side
+ height
+ box center position
+ area

correlated with 

+ gist
+ lexical recall
+ sign years
+ aoasl

Here's sample of the raw viewing data structure. 

```{r message=FALSE, warning=FALSE}
viewing <- read_csv('adultviewingspaceraw.csv', progress=FALSE) 
head(viewing, 10)
```

Now I'm going to compute the following metrics with the following definitions!

+ *upper* (75th percentile on y-axis)
+ *lower* (25th percentile on y-axis)
+ *left* (25th percentile on x-axis)
+ *right* (75th percentile on x-axis)
+ *width* (x-axis IQR)
+ *height* (y-axis IQR)
+ *x-center* (median x-axis)
+ *y-center* (median y-axis)
+ *area* (x-axis IQR * y-axis IQR)

```{r}
# compute viewing metrics
viewing_metrics <- viewing %>%
  group_by(participant, direction, media) %>%
  summarise(
    upper = quantile(y,na.rm=TRUE)[[2]],
    lower = quantile(y,na.rm=TRUE)[[4]],
    left = quantile(x,na.rm=TRUE)[[2]],
    right = quantile(x,na.rm=TRUE)[[4]],
    width = IQR(x,na.rm=TRUE),
    height = IQR(y,na.rm=TRUE),
    x_center = median(x,na.rm=TRUE),
    y_center = median(y,na.rm=TRUE),
    area = width*height
    )

# average across two stories for each direction
viewing_metrics <- viewing_metrics %>%
  group_by(participant, direction) %>%
  summarise_if(is.numeric, funs(mean(., na.rm = TRUE)))

head(viewing_metrics,10)
```

The participant names are different than what we've been using, so I'm going to fix that. Then I'll join the behavioral data with the viewing space metrics.

**Notes to self - which Alicia (Deaf or 2?) - add Gina in - add Jesse in - add JJ in - Laura 1 or Laura 2 - MarlaMarks or MarlaH - add Matthew - add Ranem**

Below is an example of the forward table for viewing space and behavioral data

```{r message=FALSE, warning=FALSE}
# Fix participant names
partnames <- read_csv("partnames.csv") %>% 
  distinct() %>% 
  mutate(participant = as.character(participant),
         new_participant = as.character(new_participant))
viewing_metrics_fixed <- viewing_metrics %>%
  ungroup() %>%
  mutate(participant = as.character(participant)) %>%
  left_join(partnames, by = "participant") %>%
  select(-participant) %>%
  rename(participant = new_participant) %>%
  filter(!is.na(participant)) %>%
  select(participant, direction:area)

# Get behavioral measures
eyeperf_fwrv <- fulldata %>%
  filter(eye_exclude == FALSE) %>%
  select(participant, maingroup, hearing, direction, aoasl, signyrs, acc, gist, eyes, mouth, neck, facechest) %>%
  group_by(maingroup, participant, direction) %>%
  mutate(gist = mean(gist, na.rm = TRUE),
         lex = mean(acc, na.rm = TRUE),
         eyes = mean(eyes, na.rm = TRUE),
         mouth = mean(mouth, na.rm = TRUE),
         neck = mean(neck, na.rm = TRUE),
         facechest = mean(facechest, na.rm = TRUE)) %>%
  ungroup() %>%
  select(maingroup, participant, hearing, direction, aoasl, signyrs, gist, lex) %>%
  distinct()

viewing_metrics_behav <- 
  left_join(viewing_metrics_fixed, eyeperf_fwrv, by = c("participant","direction")) %>%
  filter(!is.na(maingroup)) %>%
  select(participant, direction, maingroup:lex, upper:area) %>%
  gather(metric, value, gist:area) %>%
  unite(metricvalue, c(metric, direction), sep = "_", remove = FALSE) %>%
  select(-metric) %>%
  spread(metricvalue, value) %>%
  select(-participant, -maingroup, -hearing)

viewing_fw <- viewing_metrics_behav %>% 
  filter(direction == "forward") %>% 
  select(aoasl,
         signyrs,
         gist_forward, 
         lex_forward, 
         upper_forward, 
         lower_forward, 
         left_forward, 
         right_forward, 
         width_forward, 
         height_forward, 
         x_center_forward, 
         y_center_forward,
         area_forward)
viewing_rv <- viewing_metrics_behav %>% 
  filter(direction == "reversed") %>% 
  select(aoasl,
         signyrs,
         gist_reversed, 
         lex_reversed, 
         upper_reversed, 
         lower_reversed, 
         left_reversed, 
         right_reversed, 
         width_reversed, 
         height_reversed, 
         x_center_reversed, 
         y_center_reversed,
         area_reversed)

head(viewing_fw,10)
```

I'll show the pretty correlations table first. Forward and reversed in that order. 

```{r pretty correlations for viewing, rows.print = 15}
corstarsl(viewing_fw)
corstarsl(viewing_rv)
```

Now the Pearson's r correlations and p-values for forward and reversed stories in that order. 
```{r}
# Correlations for FW
print("FW Correlations - Pearson's r")
Hmisc::rcorr(as.matrix(viewing_fw))$r
print("FW Correlations - P-values")
Hmisc::rcorr(as.matrix(viewing_fw))$P
cat(paste("","\n",""))

# Correlations for RV
print("RV Correlations - Pearson's r")
Hmisc::rcorr(as.matrix(viewing_rv))$r
print("RV Correlations - P-values")
Hmisc::rcorr(as.matrix(viewing_rv))$P
cat(paste("","\n",""))
```

